Methods for detecting disease using analysis of rna

ABSTRACT

Methods for measuring subpopulations of ribonucleic acid (RNA) molecules are provided. In some embodiments, methods of generating a sequencing library from a plurality of RNA molecules in a test sample obtained from a subject are provided, as well as methods for analyzing the sequencing library to detect, e.g., the presence or absence of a disease.

CROSS-REFERENCE

This application claims the benefit of U.S. Provisional Application No.62/781,512, filed Dec. 18, 2018, and U.S. Provisional Application No.62/843,109, filed May 3, 2019, which applications are incorporatedherein by reference in their entireties for all purposes.

BACKGROUND OF THE INVENTION

With a total of over 1.6 million new cases each year in the UnitedStates as of 2017, cancer represents a prominent worldwide public healthproblem. See, Siegel et al., 2017, “Cancer statistics,” CA Cancer JClin. 67(1):7-30. Screening programs and early diagnosis have animportant impact in improving disease-free survival and reducingmortality in cancer patients. As noninvasive approaches for earlydiagnosis foster patient compliance, they can be included in screeningprograms.

Cell-free nucleic acids (cfNAs) can be found in serum, plasma, urine,and other body fluids (Chan et al., “Clinical Sciences Reviews Committeeof the Association of Clinical Biochemists Cell-free nucleic acids inplasma, serum and urine: a new tool in molecular diagnosis,” Ann ClinBiochem. 2003; 40(Pt 2):122-130) representing a “liquid biopsy,” whichis a circulating picture of a specific disease. See, De Mattos-Arrudaand Caldas, 2016, “Cell-free circulating tumour DNA as a liquid biopsyin breast cancer,” Mol Oncol. 2016; 10(3):464-474. Similarly, cell-freeRNA has been proposed as a possible analyte for cancer detection. See,Tzimagiorgis, et al., “Recovering circulating extracellular or cell-freeRNA from bodily fluids,” Cancer Epidemiology 2011; 35(6):580-589. Theseapproaches represent potential non-invasive methods of screening for avariety of diseases, such as cancers.

Nevertheless, cancer remains a frequent cause of death worldwide. Overthe last several decades, treatment options have improved, yet survivalrates remain low. The success of treatment by surgical resection anddrug-based approaches is strongly dependent on identification ofearly-stage tumors. However, current technologies, such as imaging andbiomarker-based approaches, frequently cannot identify tumors until themore advanced stages of the disease have set in.

Non-alcoholic steatohepatitis (NASH) is a disease of the livercharacterized by inflammation and damage to the liver cells. Typically,NASH and related diseases, such as NAFLD (Nonalcoholic Fatty LiverDisease), involve inflammation of the liver related to fat accumulation,and mimic alcoholic hepatitis but are observed in patients who seldom ornever consume alcohol. NASH and NAFLD are frequently reported in bothmen and women, although it most often appears in women and is especiallyprevalent in the obese. Although the disease has been observed to beaccompanied by several other pathological conditions, including diabetesmellitus, hyperlipidemia, hyperglycemia, all part of the “metabolicsyndrome,” the cause and progression of the disease, as well as thecausal or temporal relation to these conditions, is not well understood.However, in patients suffering from NAFLD and NASH in particular,certain characteristics of liver tissue and abnormalities of functionare typical. Specifically, fatty deposits, tissue degeneration,inflammation, cell degeneration, cirrhosis, elevation of free fattyacids and other such abnormalities have come to be associated withnonalcoholic steatohepatitis and are frequently seen in patientssuffering from forms of NAFLD.

Currently, liver biopsy is used in the clinical practice as the primarymethod for detection of liver ailments associated with NAFLD, NASH,fibrosis and cirrhosis. However, the use of an invasive liver biopsy asthe primary means for assessing liver disease conditions discouragesdiagnosis, and thus, subsequent treatment, as biopsy tends to beinvasive, painful, expensive, subject to sampling error and may not bepossible for all patients.

Accordingly, there remains a need for new non-invasive detectionmodalities that can identify disease at the earliest stages, whentherapeutic interventions have a greater chance of success. The currentinvention meets these, and other needs.

SUMMARY OF THE INVENTION

In various aspects, the present disclosure provides methods andcompositions for detecting a disease state of a subject. In embodiments,the methods comprise detecting one or more markers in cell-freeribonucleic acid (cfRNA). In embodiments, detecting cfRNA comprisessequencing cfRNA from a biological sample from a subject to producecfRNA reads. In embodiments, the method further comprises sequencing RNAfrom cells of a subject to produce cellular reads, and filtering thecfRNA reads to exclude cfRNA reads corresponding to one or more cellularreads. In embodiments, the cells are blood cells. In embodiments, themethods comprise filtering the cfRNA reads to exclude one or moreribosomal, mitochondrial, and/or blood-related transcripts. Inembodiments, only cfRNAs reads (or read pairs) that overlap an exon-exonjunction are measured. In embodiments, cfRNA corresponding to one ormore markers are measured (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 20, 25, or more markers). The one or more markers can be anyof the markers disclosed herein, in any combination. In embodiments, theone or more markers are associated with the disease state. Inembodiments, methods comprise treating the disease state of a subject.

Aspects of the invention include methods for detecting a disease statein a subject, the method comprising: isolating a biological test samplefrom the subject, wherein the biological test sample comprises aplurality of cell-free ribonucleic acid (cfRNA) molecules; extractingthe plurality of cfRNA molecules from the biological test sample;performing a sequencing procedure on the extracted cfRNA molecules togenerate a plurality of sequence reads; performing a filtering procedureto generate an excluded population of sequence reads that originate fromone or more healthy cells, and a non-excluded population of sequencereads; performing a quantification procedure on the non-excludedsequence reads; and detecting the disease state in the subject when thequantification procedure produces a value that exceeds a threshold. Inembodiments, detecting one or more non-excluded sequence reads above athreshold comprises (i) detection, (ii) detection above background, or(iii) detection at a level that is greater than a level of correspondingsequence reads in subjects that do not have the condition.

Aspects of the invention further include computer-implemented methodsfor identifying one or more RNA sequences indicative of a disease state,the method comprising: obtaining, by a computer system, a first set ofsequence reads from a plurality of RNA molecules from a first testsample from a subject known to have the disease, wherein the first testsample comprises a plurality of cell-free RNA (cfRNA) molecules;obtaining, by a computer system, a second set of sequence reads from aplurality of RNA molecules from a control sample; detecting, by acomputer system, one or more RNA sequences that are present in the firstset of sequence reads, and that are not present in the second set ofsequence reads, to identify one or more RNA sequences that areindicative of the disease state.

In other aspects, the invention is directed to computer-implementedmethods for detecting one or more tumor-derived RNA molecules in asubject, the method comprising: obtaining, by a computer system, a firstset of sequence reads from a plurality of RNA molecules from a firsttest sample from a subject known to have, or suspected of having, atumor, wherein the first test sample comprises a plurality of cell-freeRNA (cfRNA) molecules; obtaining, by a computer system, a second set ofsequence reads from a plurality of RNA molecules from a plurality ofblood cells from the subject; and detecting, by a computer system, oneor more RNA sequences that are present in the first set of sequencereads, and that are not present in the second set of sequence reads, todetect the one or more tumor-derived RNA molecules in the subject.

In other aspects, the invention is directed to methods for detecting apresence of a cancer, determining a cancer stage, monitoring a cancerprogression, and/or determining a cancer type or cancer subtype in asubject known to have or suspected of having a cancer, the methodcomprising: (a) obtaining a biological test sample from the subject,wherein the biological test sample comprises a plurality of cell-freeribonucleic acid (cfRNA) molecules; (b) quantitatively detecting thepresence of one or more nucleic acid sequences derived from one or moretarget RNA molecules in the biological test sample to determine a tumorRNA score, wherein the one or more target RNA molecules are selectedfrom the target RNA molecules listed on any one of Tables 1-3; and (c)detecting the presence of the cancer, determining the cancer stage,monitoring the cancer progression, and/or determining the cancer type orsubtype in the subject when the tumor RNA score exceeds a thresholdvalue.

In other aspects, the invention is directed to computer-implementedmethods for detecting the presence of a cancer in a subject, the methodcomprising: receiving a data set in a computer comprising a processorand a computer-readable medium, wherein the data set comprises aplurality of sequence reads obtained from a plurality of ribonucleicacid (RNA) molecules in a biological test sample from the subject, andwherein the computer-readable medium comprises instructions that, whenexecuted by the processor, cause the computer to: determine anexpression level of a plurality of target RNA molecules in thebiological test sample; compare the expression level of each of theplurality of target RNA molecules to an RNA tissue score matrix todetermine a cancer indicator score for each of the plurality of targetRNA molecule; aggregating the cancer indicator score for each of theplurality of target RNA molecule to generate a cancer indicator scorefor the biological test sample; and detecting the presence of the cancerin the subject when the cancer indicator score for the biological testsample exceeds a threshold value.

In other aspects, the invention is directed to methods for detecting apresence of a liver disease, determining a stage of a liver disease,and/or monitoring progression of a liver disease, the method comprising:(a) obtaining a biological test sample from the subject, wherein thebiological test sample comprises a plurality of cell-free ribonucleicacid (cfRNA) molecules; (b) quantitatively detecting the presence of anucleic acid sequence derived from one or more target RNA molecules inthe test sample to determine an RNA score from the one or more targetRNA molecules, wherein the one or more target RNA molecules is derivedfrom the AKR1B10 gene; and (c) detecting the presence of a liverdisease, determining a stage of a liver disease, and/or monitoringprogression of a liver disease in the subject when the tumor RNA scoreexceeds a threshold value.

In other aspects, the invention is directed to computer-implementedmethods for detecting the presence of a liver disease in a subject, themethod comprising: receiving a data set in a computer comprising aprocessor and a computer-readable medium, wherein the data set comprisesa plurality of sequence reads obtained from a plurality of ribonucleicacid (RNA) molecules in a biological test sample from the subject, andwherein the computer-readable medium comprises instructions that, whenexecuted by the processor, cause the computer to: determining anexpression level for each of a plurality of target RNA molecules in thebiological test sample; comparing the expression level of each of thetarget RNA molecules to an RNA tissue score matrix to determine a liverdisease indicator score for each target RNA molecule; aggregating theliver disease indicator score for each target RNA molecule to generate aliver indicator score for the biological test sample; and detecting thepresence of the liver disease in the subject when the liver diseaseindicator score for the biological test sample exceeds a thresholdvalue.

In still other aspects, the invention is directed to methods forconstructing an RNA tissue score matrix, the method comprising:compiling a plurality of RNA sequence reads obtained from a plurality ofsubjects to generate an RNA expression matrix; and normalizing the RNAexpression matrix with a tissue-specific RNA expression matrix toconstruct the RNA tissue score matrix. In some embodiments, the RNAsequence reads are obtained from a plurality of subjects having a knowncancer type to construct a cancer RNA tissue score matrix. In otherembodiments, the RNA sequence reads are obtained from a plurality ofsubjects having a known liver disease to construct a liver disease RNAtissue score matrix.

In some aspects, the present invention provides methods of measuring asubpopulation of cell-free RNA (cfRNA) molecules of a subject. Inembodiments, the method comprises (a) sequencing the cfRNA molecules toproduce cfRNA sequence reads; (b) sequencing cellular RNA extracted fromcells of the subject to produce cellular sequence reads; (c) performinga filtering procedure to produce a non-excluded population of cfRNAsequence reads, wherein the filtering comprises excluding cfRNA sequencereads that match one or more of the cellular sequence reads; and (d)quantifying one or more of the non-excluded sequence reads.

In some aspects, the present invention provides methods of detectingcancer in a subject. In embodiments, the method comprises: (a) measuringa plurality of target cell-free RNA (cfRNA) molecules in a sample of thesubject, wherein the plurality of target cfRNA molecules are selectedfrom transcripts of Tables 1-7; and (b) detecting the cancer, whereindetecting the cancer comprises detecting one or more of the target cfRNAmolecules above a threshold level. In embodiments, detecting one or morenon-excluded sequence reads above a threshold comprises (i) detection,(ii) detection above background, or (iii) detection at a level that isgreater than a level of corresponding sequence reads in subjects that donot have the condition.

In some aspects, the present invention provides methods of identifyingcancer biomarkers (also referred to herein as “markers”) in samplescollected from one or more subjects. In embodiments, the methodcomprises: (a) sequencing cfRNA of a biological fluid collected fromsubjects without cancer to produce non-cancer sequencing reads; (b) fora plurality of matched samples collected from one or more subjects witha cancer: (i) sequencing DNA and RNA collected from a cancer tissue of amatched sample to produce sequencing reads for the cancer tissue; (ii)sequencing cfDNA and cfRNA collected from a matched biological fluid ofthe matched sample to produce sequencing reads for the matchedbiological fluid; (iii) measuring a tumor fraction by relating counts ofcfDNA sequencing reads for the matched biological fluid to correspondingcounts of DNA sequencing reads for the cancer tissue; and (iv) measuringtumor content for one or more candidate biomarkers by multiplying acount of the RNA sequencing reads for the one or more candidatebiomarkers by the tumor fraction, wherein the one or more candidatebiomarkers are expressed at a higher level in the matched biologicalfluid than in the biological fluid collected from the subjects withoutcancer; (c) modeling expression of the one or more candidate biomarkersin cfRNA using the tumor content as a covariate; and (d) identifying oneor more cfRNA cancer biomarkers from among the one or more candidatebiomarkers based on the modeling.

In some aspects, the present invention provides computer systems forimplementing one or more steps in methods of any of the various aspectsdisclosed herein.

In some aspects, the present invention provides non-transitorycomputer-readable media, having stored thereon computer-readableinstructions for implementing one or more steps in methods of any of thevarious aspects disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is flowchart of a method for preparing a nucleic acid sample forsequencing according to one embodiment.

FIG. 2 is a flow diagram illustrating a method for identifying one ormore RNA sequences indicative of a disease state, in accordance with oneembodiment of the present invention.

FIG. 3 is a flow diagram illustrating a method for identifying one ormore tumor-derived RNA sequences, in accordance with one embodiment ofthe present invention.

FIG. 4 is a flow diagram illustrating a method for detecting thepresence of cancer, determining a state of cancer, monitoring cancerprogression, and/or determining cancer type in a subject, in accordancewith one embodiment of the present invention.

FIG. 5 is a flow diagram illustrating a method for detecting a diseasestate from one or more sequence reads derived from one or more targetedRNA molecules, in accordance with one embodiment of the presentinvention.

FIG. 6 is a flow diagram illustrating a method for detecting thepresence of cancer in a subject based on a cancer indicator score, inaccordance with one embodiment of the present invention.

FIG. 7 is a flow diagram illustrating a method for detecting a presenceof a liver disease, determining a stage of a liver disease, and/ormonitoring progression of a liver disease in a subject, in accordancewith one embodiment of the present invention

FIG. 8 is a flow diagram illustrating a method for detecting thepresence of cancer in a subject based on a liver disease indicatorscore, in accordance with one embodiment of the present invention.

FIG. 9 depicts the expression levels of 20 dark channel genes in lungcancer with the highest expression level ratio between cancerous andnon-cancerous samples. Reads per million (RPM) are plotted as a functionof dark channel genes. In each plot, the columns of dots from left toright correspond to groups indicated in the top legend from left toright, respectively (class, anorectal, breast, colorectal, lung, andnon-cancer).

FIG. 10 is a ROC curve of the decision tree classifier using a tissuescore aggregated from dark channel genes.

FIG. 11 is a flowchart illustrating a method in accordance with someembodiments.

FIG. 12A is a scatter plot of an example PCA (principal componentanalysis) of stage III TCGA (The Cancer Genome Atlas) FFPE(formalin-fixed paraffin embedded) tissue RNA-seq data. Gene expressionlevels are plotted in read per million.

FIG. 12B is scatter plot showing example results of CCGA (CirculatingCell-free Genome Atlas) tumor tissue RNA-seq data, projected on TCGA PCAaxes. Gene expression levels are plotted in read per million.

FIG. 12C is a scatter plot showing example results of CCGA cancercell-free RNA (cfRNA) RNA-seq data projected on TCGA PCA axes. Geneexpression levels are plotted in read per million.

FIG. 13 is a heatmap of example dark channel biomarker genes. Eachcolumn depicts one cfRNA sample, and each row depicts one gene. Thecolor of the rows encodes tissue-specificity (from top to bottom, thetissues are, respectively: breast, lung, and non-specific). The color ofthe columns encodes the sample groups (from left to right, the cancertypes are, respectively: anorectal, breast, colorectal, lung, andnon-cancer).

FIG. 14A shows box plots depicting cfRNA expression levels and tissueexpression levels of two example breast dark channel biomarkers (DCB)genes (FABP7 and SCGB2A2) in different samples: HER2+, HR+/HER2−, triplenegative breast cancer (TNBC), or non-cancer samples.

FIG. 14B shows box plots depicting cfRNA expression levels and tissueexpression levels of four example lung DCB genes (SLC34A2, ROS1, SFTPA2,and CXCL17) in different samples: adenocarcinoma, small cell lungcancer, squamous cell carcinoma, or non-cancer samples.

FIG. 15A shows forest plots depicting the detectability of two breastDCB genes (FABP7 and SCGB2A2) for breast cancer samples with matchedtumor tissue. The samples IDs are plotted based on their relative tumorfraction in cell-free DNA (cfDNA) (95% CI). FABP7 was detected insamples 4653, 4088, 2037, 3116, and 1202. SCGB2A2 was detected insamples 1656, 2419, 3911, 2367, 2037, 1039, 2139, and 3162. Tumorfraction in cfDNA was measured from SNV allele fractions from the cfDNAenrichment assay.

FIG. 15B shows forest plots depicting the detectability of two breastDCB genes (FABP7 and SCGB2A2) for breast cancer samples with matchedtumor tissue. Sample IDs are plotted as a function of tumor content(tumor fraction*tumor tissue expression). FABP7 was detected in samples4088, 1202, 3116, and 2037. SCGB2A2 was detected in samples 1656, 2419,2367, 3911, 1039, 2139, 3162, and 2037. Tumor fraction in cfDNA wasmeasured from SNV allele fractions from the cfDNA enrichment assay.Tissue expression was measured from RNA-seq data of matched tumortissue.

FIGS. 16A-D illustrate example sequencing results for DCB geneexpression in cfRNA and matched tissue for the indicated genes forsubjects with breast cancer, lung cancer, or no cancer (normal). Thenumber of read counts is represented on the y-axis.

FIGS. 17A-B illustrate example classifier workflows.

FIGS. 18A-C illustrate ROC plots showing sensitivity and specificity ofexample classification schemes.

FIG. 19 illustrates a sample processing and parameter determinationmethod, in accordance with one embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Before the present invention is described in greater detail, it is to beunderstood that this invention is not limited to particular embodimentsdescribed, as such may, of course, vary. It is also to be understoodthat the terminology used herein is for the purpose of describingparticular embodiments only, and is not intended to be limiting, sincethe scope of the present invention will be limited only by the appendedclaims.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit, unlessthe context clearly dictates otherwise, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range is encompassed within the invention. The upper and lowerlimits of these smaller ranges may independently be included in thesmaller ranges encompassed within the invention, subject to anyspecifically excluded limit in the stated range.

Unless defined otherwise, technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Singleton et al., Dictionary ofMicrobiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York,N.Y. 1994), provides one skilled in the art with a general guide to manyof the terms used in the present application, as do the following, eachof which is incorporated by reference herein in its entirety: Kornbergand Baker, DNA Replication, Second Edition (W.H. Freeman, New York,1992); Lehninger, Biochemistry, Second Edition (Worth Publishers, NewYork, 1975); Strachan and Read, Human Molecular Genetics, Second Edition(Wiley-Liss, New York, 1999); Abbas et al, Cellular and MolecularImmunology, 6th edition (Saunders, 2007).

All publications mentioned herein are expressly incorporated herein byreference to disclose and describe the methods and/or materials inconnection with which the publications are cited.

The terms “polynucleotide”, “nucleic acid” and “oligonucleotide” areused interchangeably. They refer to a polymeric form of nucleotides ofany length, either deoxyribonucleotides or ribonucleotides, or analogsthereof. Polynucleotides may have any three dimensional structure, andmay perform any function, known or unknown. The following arenon-limiting examples of polynucleotides: coding or non-coding regionsof a gene or gene fragment, loci (locus) defined from linkage analysis,exons, introns, messenger RNA (mRNA), transfer RNA (tRNA), ribosomal RNA(rRNA), short interfering RNA (siRNA), short-hairpin RNA (shRNA),micro-RNA (miRNA), ribozymes, cDNA, recombinant polynucleotides,branched polynucleotides, plasmids, vectors, isolated DNA of anysequence, isolated RNA of any sequence, nucleic acid probes, andprimers. A polynucleotide may comprise one or more modified nucleotides,such as methylated nucleotides and nucleotide analogs. If present,modifications to the nucleotide structure may be imparted before orafter assembly of the polymer. The sequence of nucleotides may beinterrupted by non-nucleotide components. A polynucleotide may befurther modified after polymerization, such as by conjugation with alabeling component.

In general, the term “target polynucleotide” refers to a nucleic acidmolecule or polynucleotide in a starting population of nucleic acidmolecules having a target sequence whose presence, amount, and/ornucleotide sequence, or changes in one or more of these, are desired tobe determined. In general, the term “target sequence” refers to anucleic acid sequence on a single strand of nucleic acid. The targetsequence may be a portion of a gene, a regulatory sequence, genomic DNA,cDNA, RNA including mRNA, miRNA, rRNA, or others. The target sequencemay be a target sequence from a sample or a secondary target such as aproduct of an amplification reaction.

The terms “marker” and “biomarker” are used interchangeably herein torefer to a polynucleotide (e.g., a gene or an identifiable sequencefragment thereof) the level or concentration of which is associated witha particular biological state (e.g., a disease state, such as presenceof cancer in general, or a particular cancer type and/or stage). Inembodiments, a marker is a cfRNA of a particular gene, changes in thelevel of which may be detected by sequencing. cfRNA biomarkers may bereferred to herein with reference to the gene from which the cfRNAderives, but does not necessitate detection of the entire genetranscript. In embodiments, only fragments of a particular genetranscript are detected. In embodiments, detecting the presence and/orlevel of a particular gene comprises detecting one or more cfRNAfragments comprising different sequence fragments (overlapping ornon-overlapping) derived from transcripts of the same gene, which may bescored collectively as part of the same “biomarker.” Additionalinformation relating to recited gene designations, including sequenceinformation (e.g., DNA, RNA, and amino acid sequences), full names ofgenes commonly identified by way of acronym, and the like are availablein publicly accessible databases known to those skilled in the art, suchas databases available from the National Center for BiotechnologyInformation (www.ncbi.nlm.nih.gov/), including GenBank(www.ncbi.nlm.nih.gov/genbank/) and the NCBI Protein database(www.ncbi.nlm.nih.gov/protein/), and UniProt (www.uniprot.org).

The term “amplicon” as used herein means the product of a polynucleotideamplification reaction; that is, a clonal population of polynucleotides,which may be single stranded or double stranded, which are replicatedfrom one or more starting sequences. The one or more starting sequencesmay be one or more copies of the same sequence, or they may be a mixtureof different sequences. Preferably, amplicons are formed by theamplification of a single starting sequence. Amplicons may be producedby a variety of amplification reactions whose products comprisereplicates of the one or more starting, or target, nucleic acids. In oneaspect, amplification reactions producing amplicons are“template-driven” in that base pairing of reactants, either nucleotidesor oligonucleotides, have complements in a template polynucleotide thatare required for the creation of reaction products. In one aspect,template-driven reactions are primer extensions with a nucleic acidpolymerase, or oligonucleotide ligations with a nucleic acid ligase.Such reactions include, but are not limited to, polymerase chainreactions (PCRs), linear polymerase reactions, nucleic acidsequence-based amplification (NASBAs), rolling circle amplifications,and the like, disclosed in the following references, each of which areincorporated herein by reference herein in their entirety: Mullis et al,U.S. Pat. Nos. 4,683,195; 4,965,188; 4,683,202; 4,800,159 (PCR); Gelfandet al, U.S. Pat. No. 5,210,015 (real-time PCR with “taqman” probes);Wittwer et al, U.S. Pat. No. 6,174,670; Kacian et al, U.S. Pat. No.5,399,491 (“NASBA”); Lizardi, U.S. Pat. No. 5,854,033; Aono et al,Japanese patent publ. JP 4-262799 (rolling circle amplification); andthe like. In one aspect, amplicons of the invention are produced byPCRs. An amplification reaction may be a “real-time” amplification if adetection chemistry is available that permits a reaction product to bemeasured as the amplification reaction progresses, e.g., “real-timePCR”, or “real-time NASBA” as described in Leone et al, Nucleic AcidsResearch, 26: 2150-2155 (1998), and like references.

The term “amplifying” means performing an amplification reaction. A“reaction mixture” means a solution containing all the necessaryreactants for performing a reaction, which may include, but is not belimited to, buffering agents to maintain pH at a selected level during areaction, salts, co-factors, scavengers, and the like.

The terms “fragment” or “segment”, as used interchangeably herein, referto a portion of a larger polynucleotide molecule. A polynucleotide, forexample, can be broken up, or fragmented into, a plurality of segments,either through natural processes, as is the case with, e.g., cfDNAfragments that can naturally occur within a biological sample, orthrough in vitro manipulation. Various methods of fragmenting nucleicacid are well known in the art. These methods may be, for example,either chemical or physical or enzymatic in nature. Enzymaticfragmentation may include partial degradation with a DNase; partialdepurination with acid; the use of restriction enzymes; intron-encodedendonucleases; DNA-based cleavage methods, such as triplex and hybridformation methods, that rely on the specific hybridization of a nucleicacid segment to localize a cleavage agent to a specific location in thenucleic acid molecule; or other enzymes or compounds which cleave apolynucleotide at known or unknown locations. Physical fragmentationmethods may involve subjecting a polynucleotide to a high shear rate.High shear rates may be produced, for example, by moving DNA through achamber or channel with pits or spikes, or forcing a DNA sample througha restricted size flow passage, e.g., an aperture having a crosssectional dimension in the micron or submicron range. Other physicalmethods include sonication and nebulization. Combinations of physicaland chemical fragmentation methods may likewise be employed, such asfragmentation by heat and ion-mediated hydrolysis. See, e.g., Sambrooket al., “Molecular Cloning: A Laboratory Manual,” 3rd Ed. Cold SpringHarbor Laboratory Press, Cold Spring Harbor, N.Y. (2001) (“Sambrook etal.) which is incorporated herein by reference for all purposes. Thesemethods can be optimized to digest a nucleic acid into fragments of aselected size range.

The terms “polymerase chain reaction” or “PCR”, as used interchangeablyherein, mean a reaction for the in vitro amplification of specific DNAsequences by the simultaneous primer extension of complementary strandsof DNA. In other words, PCR is a reaction for making multiple copies orreplicates of a target nucleic acid flanked by primer binding sites,such reaction comprising one or more repetitions of the following steps:(i) denaturing the target nucleic acid, (ii) annealing primers to theprimer binding sites, and (iii) extending the primers by a nucleic acidpolymerase in the presence of nucleoside triphosphates. Usually, thereaction is cycled through different temperatures optimized for eachstep in a thermal cycler instrument. Particular temperatures, durationsat each step, and rates of change between steps depend on many factorsthat are well-known to those of ordinary skill in the art, e.g.,exemplified by the following references: McPherson et al, editors, PCR:A Practical Approach and PCR2: A Practical Approach (IRL Press, Oxford,1991 and 1995, respectively). For example, in a conventional PCR usingTaq DNA polymerase, a double stranded target nucleic acid may bedenatured at a temperature >90° C., primers annealed at a temperature inthe range 50-75° C., and primers extended at a temperature in the range72-78° C. The term “PCR” encompasses derivative forms of the reaction,including, but not limited to, RT-PCR, real-time PCR, nested PCR,quantitative PCR, multiplexed PCR, and the like. The particular formatof PCR being employed is discernible by one skilled in the art from thecontext of an application. Reaction volumes can range from a few hundrednanoliters, e.g., 200 nL, to a few hundred μL, e.g., 200 μL. “Reversetranscription PCR,” or “RT-PCR,” means a PCR that is preceded by areverse transcription reaction that converts a target RNA to acomplementary single stranded DNA, which is then amplified, an exampleof which is described in Tecott et al, U.S. Pat. No. 5,168,038, thedisclosure of which is incorporated herein by reference in its entirety.“Real-time PCR” means a PCR for which the amount of reaction product,i.e., amplicon, is monitored as the reaction proceeds. There are manyforms of real-time PCR that differ mainly in the detection chemistriesused for monitoring the reaction product, e.g., Gelfand et al, U.S. Pat.No. 5,210,015 (“taqman”); Wittwer et al, U.S. Pat. Nos. 6,174,670 and6,569,627 (intercalating dyes); Tyagi et al, U.S. Pat. No. 5,925,517(molecular beacons); the disclosures of which are hereby incorporated byreference herein in their entireties. Detection chemistries forreal-time PCR are reviewed in Mackay et al, Nucleic Acids Research, 30:1292-1305 (2002), which is also incorporated herein by reference.“Nested PCR” means a two-stage PCR wherein the amplicon of a first PCRbecomes the sample for a second PCR using a new set of primers, at leastone of which binds to an interior location of the first amplicon. Asused herein, “initial primers” in reference to a nested amplificationreaction mean the primers used to generate a first amplicon, and“secondary primers” mean the one or more primers used to generate asecond, or nested, amplicon. “Asymmetric PCR” means a PCR wherein one ofthe two primers employed is in great excess concentration so that thereaction is primarily a linear amplification in which one of the twostrands of a target nucleic acid is preferentially copied. The excessconcentration of asymmetric PCR primers may be expressed as aconcentration ratio. Typical ratios are in the range of from 10 to 100.“Multiplexed PCR” means a PCR wherein multiple target sequences (or asingle target sequence and one or more reference sequences) aresimultaneously carried out in the same reaction mixture, e.g., Bernardet al, Anal. Biochem., 273: 221-228 (1999)(two-color real-time PCR).Usually, distinct sets of primers are employed for each sequence beingamplified. Typically, the number of target sequences in a multiplex PCRis in the range of from 2 to 50, or from 2 to 40, or from 2 to 30.“Quantitative PCR” means a PCR designed to measure the abundance of oneor more specific target sequences in a sample or specimen. QuantitativePCR includes both absolute quantitation and relative quantitation ofsuch target sequences. Quantitative measurements are made using one ormore reference sequences or internal standards that may be assayedseparately or together with a target sequence. The reference sequencemay be endogenous or exogenous to a sample or specimen, and in thelatter case, may comprise one or more competitor templates. Typicalendogenous reference sequences include segments of transcripts of thefollowing genes: β-actin, GAPDH, β₂-microglobulin, ribosomal RNA, andthe like. Techniques for quantitative PCR are well-known to those ofordinary skill in the art, as exemplified in the following references,which are incorporated by reference herein in their entireties: Freemanet al, Biotechniques, 26: 112-126 (1999); Becker-Andre et al, NucleicAcids Research, 17: 9437-9447 (1989); Zimmerman et al, Biotechniques,21: 268-279 (1996); Diviacco et al, Gene, 122: 3013-3020 (1992); andBecker-Andre et al, Nucleic Acids Research, 17: 9437-9446 (1989).

The term “primer” as used herein means an oligonucleotide, eithernatural or synthetic, that is capable, upon forming a duplex with apolynucleotide template, of acting as a point of initiation of nucleicacid synthesis and being extended from its 3′ end along the template sothat an extended duplex is formed. Extension of a primer is usuallycarried out with a nucleic acid polymerase, such as a DNA or RNApolymerase. The sequence of nucleotides added in the extension processis determined by the sequence of the template polynucleotide. Usually,primers are extended by a DNA polymerase. Primers usually have a lengthin the range of from 14 to 40 nucleotides, or in the range of from 18 to36 nucleotides. Primers are employed in a variety of nucleicamplification reactions, for example, linear amplification reactionsusing a single primer, or polymerase chain reactions, employing two ormore primers. Guidance for selecting the lengths and sequences ofprimers for particular applications is well known to those of ordinaryskill in the art, as evidenced by the following reference that isincorporated by reference herein in its entirety: Dieffenbach, editor,PCR Primer: A Laboratory Manual, 2nd Edition (Cold Spring Harbor Press,New York, 2003).

The terms “subject” and “patient” are used interchangeably herein andrefer to a human or non-human animal who is known to have, orpotentially has, a medical condition or disorder, such as, e.g., acancer.

The term “sequence read” as used herein refers to a string ofnucleotides from part of, or all of, a nucleic acid molecule from asample obtained from a subject. A sequence read may be a short string ofnucleotides (e.g., 20-150) sequenced from a nucleic acid fragment, ashort string of nucleotides at one or both ends of a nucleic acidfragment, or the sequencing of the entire nucleic acid fragment thatexists in the biological sample. Sequence reads can be obtained throughvarious methods known in the art. For example, a sequence read may beobtained in a variety of ways, e.g., using sequencing techniques orusing probes, e.g., in hybridization arrays or capture probes, oramplification techniques, such as the polymerase chain reaction (PCR) orlinear amplification using a single primer or isothermal amplification.

The term “read segment” or “read” as used herein refers to anynucleotide sequences, including sequence reads obtained from a subjectand/or nucleotide sequences, derived from an initial sequence read froma sample. For example, a read segment can refer to an aligned sequenceread, a collapsed sequence read, or a stitched read. Furthermore, a readsegment can refer to an individual nucleotide base, such as a singlenucleotide variant.

The term “enrich” as used herein means to increase a proportion of oneor more target nucleic acids in a sample. An “enriched” sample orsequencing library is therefore a sample or sequencing library in whicha proportion of one of more target nucleic acids has been increased withrespect to non-target nucleic acids in the sample.

In general, the terms “cell-free,” “circulating,” and “extracellular” asapplied to polynucleotides (e.g. “cell-free RNA” and “cell-free DNA”)are used interchangeably to refer to polynucleotides present in a samplefrom a subject or portion thereof that can be isolated or otherwisemanipulated without applying a lysis step to the sample as originallycollected (e.g., as in lysis for the extraction from cells or viruses).Cell-free polynucleotides are thus unencapsulated or “free” from thecells or viruses from which they originate, even before a sample of thesubject is collected. Cell-free polynucleotides may be produced as abyproduct of cell death (e.g. apoptosis or necrosis) or cell shedding,releasing polynucleotides into surrounding body fluids or intocirculation. Accordingly, cell-free polynucleotides may be isolated froma non-cellular fraction of blood (e.g. serum or plasma), from otherbodily fluids (e.g. urine), or from non-cellular fractions of othertypes of samples. The term “cell-free RNA” or “cfRNA” refers toribonucleic acid fragments that circulate in a subject's body (e.g.,bloodstream) and may originate from one or more healthy cells and/orfrom one or more cancer cells. Likewise, “cell-free DNA” or “cfDNA”refers to deoxyribonucleic acid molecules that circulate in a subject'sbody (e.g., bloodstream) and may originate from one or more helathycells and/or from one or more cancer cells.

The term “circulating tumor RNA” or “ctRNA” refers to ribonucleic acidfragments that originate from tumor cells or other types of cancercells, which may be released into a subject's body (e.g., bloodstream)as a result of biological processes, such as apoptosis or necrosis ofdying cells, or may be actively released by viable tumor cells.

The term “dark channel RNA” or “dark channel cfRNA molecule” or “darkchannel gene” as used herein refers to an RNA molecule or gene whoseexpression in healthy cells is very low or nonexistent. Accordingly,identification, detection, and/or quantification of dark channel RNA(cfRNA) molecules improves signal-to-noise, and improvements insensitivity and specificity, in assessment of a disease state, such ascancer.

“Treating” or “treatment” as used herein (and as well-understood in theart) includes any approach for obtaining beneficial or desired resultsin a subject's condition, including clinical results. Beneficial ordesired clinical results can include, but are not limited to,alleviation or amelioration of one or more symptoms or conditions,diminishment of the extent of a disease, stabilizing (i.e., notworsening) the state of disease, prevention of a disease's transmissionor spread, delay or slowing of disease progression, amelioration orpalliation of the disease state, diminishment of the reoccurrence ofdisease, and remission, whether partial or total and whether detectableor undetectable. In other words, “treatment” as used herein includes anycure, amelioration, or prevention of a disease. Treatment may preventthe disease from occurring; inhibit the disease's spread; relieve thedisease's symptoms (e.g., ocular pain, seeing halos around lights, redeye, very high intraocular pressure), fully or partially remove thedisease's underlying cause, shorten a disease's duration, or do acombination of these things.

“Treating” and “treatment” as used herein includes prophylactictreatment. Treatment methods include administering to a subject atherapeutically effective amount of an active agent. The administeringstep may consist of a single administration or may include a series ofadministrations. The length of the treatment period depends on a varietyof factors, such as the severity of the condition, the age of thepatient, the concentration of active agent, the activity of thecompositions used in the treatment, or a combination thereof. It willalso be appreciated that the effective dosage of an agent used for thetreatment or prophylaxis may increase or decrease over the course of aparticular treatment or prophylaxis regime. Changes in dosage may resultand become apparent by standard diagnostic assays known in the art. Insome instances, chronic administration may be required. For example, thecompositions are administered to the subject in an amount and for aduration sufficient to treat the patient. In embodiments, the treatingor treatment is no prophylactic treatment.

The term “prevent”, as pertains to a disease or condition of a subject,refers to a decrease in the occurrence of one or more correspondingsymptoms in the subject. As indicated above, the prevention may becomplete (no detectable symptoms) or partial, such that fewer symptomsare observed, and/or with lower incidence, than would likely occurabsent treatment.

Aspects of the invention include methods for detecting a disease state,(e.g., a presence or absence of cancer), and/or a tissue of origin ofthe disease in a subject, based on analysis of one or more RNA moleculesin a sample from the subject. In some embodiments, a method fordetecting a disease state in a subject comprises isolating a biologicaltest sample from the subject, wherein the biological test samplecomprises a plurality of cell-free ribonucleic acid (cfRNA) molecules,extracting the cfRNA molecules from the biological test sample,performing a sequencing procedure on the extracted cfRNA molecules togenerate a plurality of sequence reads, performing a filtering procedureto generate an excluded population of sequence reads that originate fromone or more healthy cells, and a non-excluded population of sequencereads, and performing a quantification procedure on the non-excludedsequence reads. In embodiments, the methods comprise detecting thedisease state in the subject when the quantification procedure producesa value that exceeds a threshold. In embodiments, detecting one or morenon-excluded sequence reads above a threshold comprises (i) detection,(ii) detection above background, or (iii) detection at a level that isgreater than a level of corresponding sequence reads in subjects that donot have the condition. In certain embodiments, the threshold value isan integer that ranges from about 1 to about 10, such as about 2, 3, 4,5, 6, 7, 8, or about 9. In some embodiments, the threshold is anon-integer value, ranging from about 0.1 to about 0.9, such as about0.2, 0.3, 0.4, 0.5, 0.6, 0.7 or about 0.8.

In some embodiments, the methods involve the use of sequencing procedurefor detecting and quantifying the cfRNA molecules that are extractedfrom a biological test sample. For example, in certain embodiments asequencing procedure involves performing a reverse transcriptionprocedure on the cfRNA molecules to produce a plurality of cDNA/RNAhybrid molecules, degrading the RNA of the hybrid molecules to produce aplurality of single-stranded cDNA molecule templates, synthesizing aplurality of double-stranded DNA molecules from the single-stranded cDNAmolecule templates, ligating a plurality of double-stranded DNA adaptersto the plurality of double-stranded DNA molecules producing a sequencinglibrary, and performing a sequencing procedure on at least a portion ofthe sequencing library to obtain a plurality of sequence reads. Incertain embodiments, synthesizing the double-stranded DNA moleculesinvolves performing a strand-displacement reverse transcriptaseprocedure.

In some embodiments, the methods utilize whole transcriptome sequencingprocedures. In other embodiments, a sequencing procedure involves atargeted sequencing procedure, wherein one or more of the cfRNAmolecules are enriched from the biological test sample before preparinga sequencing library. In accordance with this embodiment, one or morecfRNA molecules indicative of the disease state are targeted forenrichment. For example, in some embodiments, the one or more targetedcfRNA molecules are derived from one or more genes selected from thegroup consisting of: AGR2, BPIFA1, CASP14, CSN1S1, DISP2, EIF2D, FABP7,GABRG1, GNAT3, GRHL2, HOXC10, IDI2-AS1, KRT16P2, LALBA, LINC00163,NKX2-1, OPN1SW, PADI3, PTPRZ1, ROS1, S100A7, SCGB2A2, SERPINB5, SFTA3,SFTPA2, SLC34A2, TFF1, VTCN1, WFDC2, MUC5B, SMIM22, CXCL17, RNU1-1, andKLK5. In some embodiments, one or more target RNA molecules are derivedfrom one or more genes selected from the group consisting of ROS1,NKX2-1, GGTLC1, SLC34A2, SFTPA2, BPIFA1, SFTA3, GABRG1, AGR2, GNAT3,MUC5B, SMIM22, CXCL17, and WFDC2. In some embodiments, one or moretarget RNA molecules are derived from one or more genes selected fromthe group consisting of SCGB2A2, CSN1S1, VTCN, FABP7, LALBA, RNU1-1,OPN1SW, CASP14, KLK5, and WFDC2. In some embodiments, one or more targetRNA molecules are derived from one or more genes selected from the groupconsisting of CASP14, CRABP2, FABP7, SCGB2A2, SERPINB5, TRGV10, VGLL1,TFF1, and AC007563.5. In still other embodiments, the targeted RNAmolecule is derived from the AKR1B10, C3, and/or PIEXO2 gene(s).

Aspects of the invention involve analysis of one or more dark channelRNA molecules, whose expression in the plasma of healthy subjects isvery low or nonexistent. Due to their low expression level in the plasmaof healthy subjects, dark channel RNA molecules provide a high signal tonoise ratio that can be used in conjunction with the present methods.

Some aspects of the invention involve filtering procedures that are usedto generate an excluded population of sequence reads that originate fromone or more healthy cells, and a non-excluded population of sequencereads that are used in subsequent analyses. In certain embodiments, thefiltering procedure involves comparing each sequence read from the cfRNAmolecules extracted from the biological test sample to a control dataset of RNA sequences, identifying one or more sequence reads that matchone or more sequence reads in the control data set of RNA sequences, andplacing each sequence read that matches the one or more sequence readsin the control data set of RNA sequences in the excluded population ofsequence reads.

In some embodiments, a control data set of RNA sequences includes aplurality of sequence reads obtained from one or more healthy subjects.In some embodiments, a control data set of RNA sequences includes aplurality of sequence reads obtained from a plurality of blood cellsfrom the subject. For example, in some embodiments, a plurality ofsequence reads are obtained from a subject's white blood cells (WBCs).

Biological Samples

The present invention involves obtaining a test sample, e.g., abiological test sample, such as a tissue and/or body fluid sample, froma subject for purposes of analyzing a plurality of nucleic acids (e.g.,a plurality of cfRNA molecules) therein. Samples in accordance withembodiments of the invention can be collected in anyclinically-acceptable manner. Any sample suspected of containing aplurality of nucleic acids can be used in conjunction with the methodsof the present invention. In some embodiments, a sample can comprise atissue, a body fluid, or a combination thereof. In some embodiments, abiological sample is collected from a healthy subject. In someembodiments, a biological sample is collected from a subject who isknown to have a particular disease or disorder (e.g., a particularcancer or tumor). In some embodiments, a biological sample is collectedfrom a subject who is suspected of having a particular disease ordisorder.

As used herein, the term “tissue” refers to a mass of connected cellsand/or extracellular matrix material(s). Non-limiting examples oftissues that are commonly used in conjunction with the present methodsinclude skin, hair, finger nails, endometrial tissue, nasal passagetissue, central nervous system (CNS) tissue, neural tissue, eye tissue,liver tissue, kidney tissue, placental tissue, mammary gland tissue,gastrointestinal tissue, musculoskeletal tissue, genitourinary tissue,bone marrow, and the like, derived from, for example, a human ornon-human mammal. Tissue samples in accordance with embodiments of theinvention can be prepared and provided in the form of any tissue sampletypes known in the art, such as, for example and without limitation,formalin-fixed paraffin-embedded (FFPE), fresh, and fresh frozen (FF)tissue samples.

As used herein, the terms “body fluid” and “biological fluid” refer to aliquid material derived from a subject, e.g., a human or non-humanmammal. Non-limiting examples of body fluids that are commonly used inconjunction with the present methods include mucous, blood, plasma,serum, serum derivatives, synovial fluid, lymphatic fluid, bile, phlegm,saliva, sweat, tears, sputum, amniotic fluid, menstrual fluid, vaginalfluid, semen, urine, cerebrospinal fluid (CSF), such as lumbar orventricular CSF, gastric fluid, a liquid sample comprising one or morematerial(s) derived from a nasal, throat, or buccal swab, a liquidsample comprising one or more materials derived from a lavage procedure,such as a peritoneal, gastric, thoracic, or ductal lavage procedure, andthe like.

In some embodiments, a sample can comprise a fine needle aspirate orbiopsied tissue. In some embodiments, a sample can comprise mediacontaining cells or biological material. In some embodiments, a samplecan comprise a blood clot, for example, a blood clot that has beenobtained from whole blood after the serum has been removed. In someembodiments, a sample can comprise stool. In one preferred embodiment, asample is drawn whole blood. In one aspect, only a portion of a wholeblood sample is used, such as plasma, red blood cells, white bloodcells, and platelets. In some embodiments, a sample is separated intotwo or more component parts in conjunction with the present methods. Forexample, in some embodiments, a whole blood sample is separated intoplasma, red blood cell, white blood cell, and platelet components.

In some embodiments, a sample includes a plurality of nucleic acids notonly from the subject from which the sample was taken, but also from oneor more other organisms, such as viral DNA/RNA that is present withinthe subject at the time of sampling.

Nucleic acid can be extracted from a sample according to any suitablemethods known in the art, and the extracted nucleic acid can be utilizedin conjunction with the methods described herein. See, e.g., Maniatis,et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbor,N.Y., pp. 280-281, 1982, the contents of which are incorporated byreference herein in their entirety. In one preferred embodiment, cellfree ribonucleic acid (e.g., cfRNA) is extracted from a sample.

In embodiments, the sample is a “matched” or “paired” sample. Ingeneral, the terms “matched sample” and “paired sample” refer to a pairof samples of different types collected from the same subject,preferably at about the same time (e.g., as part of a single procedureor office visit, or on the same day). In embodiments, the differenttypes are a tissue sample (e.g., cancer tissue, as in a resection orbiopsy sample) and a biological fluid sample (e.g., blood or a bloodfraction). The terms may also be used to refer to polynucleotidesderived from the matched sample (e.g., polynucleotides extracted from acancer tissue, paired with cell-free polynucleotides from a matchedbiological fluid sample), or sequencing reads thereof. In embodiments, aplurality of paired samples are analyzed, such as in identifying cancerbiomarkers. The plurality of paired samples may be from the sameindividual collected at different times (e.g., as in a paired samplefrom an early stage of cancer, and a paired sample from a later stage ofcancer), from different individuals at the same or different times, or acombination of these. In embodiments, the matched samples are fromdifferent subjects. In embodiments, the matched samples in a pluralityare from subjects with the same cancer type, and optionally the samecancer stage.

Example Assay Protocol

FIG. 1 is flowchart of a method 100 for preparing a nucleic acid samplefor sequencing according to one embodiment. The method 100 includes, butis not limited to, the following steps. For example, any step of themethod 100 may comprise a quantitation sub-step for quality control orother laboratory assay procedures known to one skilled in the art.

In step 110, a ribonucleic acid (RNA) sample is extracted from asubject. The RNA sample may comprise the whole human transcriptome, orany subset of the human transcriptome. The sample may be extracted froma subject known to have or suspected of having a disease (e.g., cancer).The sample may include blood, plasma, serum, urine, fecal, saliva, othertypes of bodily fluids, or any combination thereof. In some embodiments,methods for drawing a blood sample (e.g., syringe or finger prick) maybe less invasive than procedures for obtaining a tissue biopsy, whichmay require surgery. The extracted sample may further comprise cfDNA. Ifa subject has a disease (e.g., cancer), cfRNA in an extracted sample maybe present at a detectable level for diagnosis.

In step 120, the nucleic acid sample including RNA molecules isoptionally treated with a DNase enzyme. The DNase may remove DNAmolecules from the nucleic acid sample to reduce DNA contamination ofthe RNA molecules. After RNA molecules are converted into DNA, it may bedifficult to distinguish the RNA-converted DNA and genomic DNAoriginally found in the nucleic acid sample. Applying the DNase allowsfor targeted amplification of molecules originating from cfRNA. TheDNase process may include steps for adding a DNase buffer, mixing thesample applied with DNase using a centrifuge, and incubation. In someembodiments, step 120 includes one or more processes based on the DNasetreatment protocol described in the Qiagen QIAamp Circulating NucleicAcid Handbook.

In step 130, a reverse transcriptase enzyme is used to convert the RNAmolecules in the nucleic acid sample into complementary DNA (cDNA). Thereverse transcriptase process may include a first-strand synthesis step(generation of a cDNA strand via reverse transcription), degradation ofthe RNA strand to produce a single-stranded cDNA molecule, and synthesisof a double-stranded DNA molecules from the single-stranded cDNAmolecule using a polymerase. During first-strand synthesis, a primeranneals to the 3′ end of a RNA molecule. During second-strand synthesis,a different primer anneals to the 3′ end of the cDNA molecule.

In step 140, a sequencing library is prepared. For example, as is wellknown in the art, adapters can be ligated to one or both ends of a dsDNAmolecule to prepare a library for sequencing. In one embodiment, theadapters utilized may include one or more sequencing oligonucleotidesfor use in subsequent cluster generation and/or sequencing (e.g., knownP5 and P7 sequences for used in sequencing by synthesis (SBS) (Illumina,San Diego, Calif.)). In another embodiment, the adapter includes asample specific index sequence, such that, after library preparation,the library can be combined with one or more other libraries preparedfrom individual samples, thereby allowing for multiplex sequencing. Thesample specific index sequence can comprise a short oligonucleotidesequence having a length of from about 2 nt to about 20 nt, from about 2nt to about 10 nt, from about 2 to about 8 nt, or from about 2 to about6 nt. In another embodiment, the sample specific index sequence cancomprise a short oligonucleotide sequence greater than about 2, 3, 4, 5,6, 7, or 8 nucleotides (nt) in length.

Optionally, during library preparation, unique molecular identifiers(UMI) can be added to the nucleic acid molecules in the sample throughadapter ligation. The UMIs are short nucleic acid sequences (e.g., 4-10base pairs) that are added to one or both ends of nucleic acid fragmentsduring adapter ligation. In some embodiments, UMIs are degenerate basepairs that serve as a unique tag that can be used to identify sequencereads originating from a specific nucleic acid fragment. During PCRamplification following adapter ligation, the UMIs are replicated alongwith the attached nucleic acid fragment, which provides a way toidentify sequence reads that came from the same original nucleic acidmolecule in downstream analysis.

For embodiments including targeted sequencing of RNA, in step 150,targeted nucleic acid sequences are enriched from the library. Duringenrichment, hybridization probes (also referred to herein as “probes”)are used to target, and pull down, nucleic acid fragments informativefor the presence or absence of a disease (e.g., cancer), disease status(e.g., cancer status), or a disease classification (e.g., cancer type ortissue of origin). For a given workflow, the probes may be designed toanneal (or hybridize) to a target (complementary) nucleic acid strand(e.g., a DNA strand converted from RNA). The probes may range in lengthfrom 10s, 100s, or 1000s of base pairs. In one embodiment, the probesare designed based on a gene panel to analyze particular target regionsof the genome (e.g., of the human or another organism) that aresuspected to correspond to certain cancers or other types of diseases.Moreover, the probes may cover overlapping portions of a target region.In other embodiments, targeted RNA molecules can be enriched usinghybridization probes prior to conversion of the RNA molecules to cDNAstrands using reverse transcriptase (not shown). In general, any knownmethod in the art can be used to isolate, and enrich for,probe-hybridized target nucleic acids. For example, as is well known inthe art, a biotin moiety can be added to the 5′-end of the probes (i.e.,biotinylated) to facilitate isolation of target nucleic acids hybridizedto probes using a streptavidin-coated surface (e.g., streptavidin-coatedbeads).

Additionally, for targeted sequencing, in step 160, sequence reads aregenerated from the enriched nucleic acid sample. Sequencing data may beacquired from the enriched DNA sequences (i.e., DNA sequences derived,or converted, from RNA sequences) by known means in the art. Forexample, the method 100 may include next generation sequencing (NGS)techniques including synthesis technology (Illumina), pyrosequencing(454 Life Sciences), ion semiconductor technology (Ion Torrentsequencing), single-molecule real-time sequencing (Pacific Biosciences),sequencing by ligation (SOLiD sequencing), nanopore sequencing (OxfordNanopore Technologies), or paired-end sequencing. In some embodiments,massively parallel sequencing is performed using sequencing-by-synthesiswith reversible dye terminators.

In other embodiments, for example, in a whole transcriptome sequencingapproach (e.g., instead of targeted sequencing), in step 170, abundantRNA species are depleted from the nucleic acid sample. For example, insome embodiments, ribosomal RNA (rRNA) and/or transfer RNA (tRNA)species can be depleted. Available commercial kits, such as RiboMinus™(ThermoFisher Scientific) or AnyDeplete (NuGen), can be used fordepletion of abundant RNA species. In an embodiment, after depletion ofnucleic acids (e.g., converted DNA) derived from abundant RNA molecules,sequence reads are generated in step 180.

In some embodiments, the sequence reads may be aligned to a referencegenome using known methods in the art to determine alignment positioninformation. The alignment position information may indicate a beginningposition and an end position of a region in the reference genome thatcorresponds to a beginning nucleotide base and end nucleotide base of agiven sequence read. Alignment position information may also includesequence read length, which can be determined from the beginningposition and end position. A region in the reference genome may beassociated with a gene or a segment of a gene. The reference genome maycomprise the whole transcriptome, or any portion thereof (e.g., aplurality of targeted transcripts). In another embodiment, the referencegenome can be the whole genome from an organism being tested andsequence reads derived from (or reverse transcribed from) extracted RNAmolecules are aligned to the reference genome to determine location,fragment length, and/or start and end positions. For example, in oneembodiment, sequence reads are aligned to human reference genome hg19.The sequence of the human reference genome, hg19, is available fromGenome Reference Consortium with a reference number, GRCh37/hg19, andalso available from Genome Browser provided by Santa Cruz GenomicsInstitute. The alignment position information may indicate a beginningposition and an end position of a region in the reference genome thatcorresponds to a beginning nucleotide base and end nucleotide base of agiven sequence read. Alignment position information may also includesequence read length, which can be determined from the beginningposition and end position. A region in the reference genome may beassociated with a gene or a segment of a gene.

Identification of Dark Channel RNA Molecules

Aspects of the invention include computer-implemented methods foridentifying one or more RNA sequences indicative of a disease state in asubject (or “dark channel RNA molecules”). In some embodiments, themethods involve obtaining, by a computer system, a first set of sequencereads from a plurality of RNA molecules from a first test sampleobtained from a subject known to have the disease, wherein the firsttest sample comprises a plurality of cell-free RNA (cfRNA) molecules,and a second set of sequence reads from a plurality of RNA moleculesfrom a control sample, detecting, one or more RNA sequences that arepresent in the first set of sequence reads, and that are not present inthe second set of sequence reads, to identify one or more RNA sequencesthat are indicative of the disease state. In some embodiments, the firsttest sample obtained from the patient comprises a bodily fluid (e.g.,blood, plasma, serum, urine, saliva, pleural fluid, pericardial fluid,cerebrospinal fluid (CSF), peritoneal fluid, or any combinationthereof). In one preferred embodiment, a test sample obtained from thepatient is a plasma sample. In some embodiments, the control samplecomprises a plurality of RNA molecules obtained from healthy cells fromthe subject (e.g., white blood cells).

FIG. 2 is a flow diagram illustrating a method for identifying one ormore RNA sequences indicative of a disease state, in accordance with oneembodiment of the present invention. As shown in FIG. 2 , at step 210, afirst set of sequence reads is obtained from a biological test samplecomprising a plurality of cell-free RNA (cfRNA) molecules. The cell-freecontaining biological test sample can be any a bodily fluid, such as,blood, plasma, serum, urine, pleural fluid, cerebrospinal fluid, tears,saliva, or ascitic fluid. In accordance with this embodiment, the cfRNAbiological test sample is obtained from a test subject known to have, orsuspected of having a disease, the cfRNA molecules extracted from thesample and sequence reads determined (as described elsewhere herein).For example, in one embodiment, a complementary DNA strand issynthesized using a reverse transcription step generating a cDNA/RNAhybrid molecule, the RNA molecule degraded, a double stranded DNAmolecule synthesized from the cDNA strand using a polymerase, asequencing library prepared, and sequence reads determined using asequencing platform. The sequencing step can be any carried out usingany known sequencing platform in the art, such as, any massivelyparallel sequencing platform, including a sequencing-by-synthesisplatform (e.g., Illumina's HiSeq X) or a sequencing-by-ligation platform(e.g. the Life Technologies SOLD platform), the Ion Torrent/Ion Proton,semiconductor sequencing, Roche 454, single molecular sequencingplatforms (e.g. Helicos. Pacific Biosciences and nanopore), aspreviously described. Alternatively, other means for detecting andquantifying the sequence reads can be used, for example, array-basedhybridization, probe-based in-solution hybridization, ligation-basedassays, primer extension reaction assays, can be used to determinesequence reads from DNA molecules (e.g., converted from RNA molecules),as one of skill in the art would readily understand.

At step 220, a second set of sequence reads is obtained from a healthycontrol sample. In one embodiment, the healthy control sample is fromthe same subject and comprises a plurality of cellular RNA molecules.For example, the control sample can be blood cells, such as white bloodcells, and the plurality of sequence reads derived from RNA moleculesextracted from the blood cells. In accordance with this embodiment, theRNA molecules are extracted from the healthy control sample (e.g., bloodcells), converted to DNA, a sequencing library prepared, and the secondset of sequence reads determined (as described elsewhere herein). Inother embodiments, the healthy control sample can be a database ofsequence data determined for RNA sequences obtained from a healthysubject, or from healthy cells.

At step 230, sequence reads from the first set of sequence reads and thesecond set of sequence reads are compared to identifying one or more RNAmolecules indicative of a disease state. Moreover, one or more sequencereads (derived from RNA molecules) present in the first set of sequencereads, and not present in the second set of sequence reads, areidentified as derived from RNA molecules indicative of a disease state.For example, the first set of sequence reads can comprise sequence readsderived from cfRNA molecules from a plasma sample obtained from asubject known to have, or suspected of having, a disease (e.g., cancer).And the second set of sequence reads can comprise sequence reads derivedfrom RNA molecules from healthy cells (e.g., white blood cells). Bycomparing, and removing, the second set of sequence reads derived fromhealthy cells from the first set of sequence reads derived from acell-free RNA sample, one can identify the sequence reads derived from adisease state (e.g., cancer).

In some embodiments, a control data set of RNA sequences includes aplurality of sequence reads obtained from one or more healthy subjects.In certain embodiments, the second set of sequence reads comprises RNAsequence information obtained from a public database. Public databasesthat can be used in accordance with embodiments of the invention includethe tissue RNA-seq database GTEx (available at gtexportal.org/home). Insome embodiments, a control data set of RNA sequences includes aplurality of sequence reads obtained from a plurality of blood cellsfrom the subject. For example, in some embodiments, a plurality ofsequence reads are obtained from a subject's white blood cells (WBCs).

Detection of Tumor-Derived RNA Molecules

Aspects of the invention include computer-implemented methods fordetecting one or more tumor-derived RNA molecules in a subject. In someembodiments, the methods involve: obtaining, by a computer system, afirst set of sequence reads from a plurality of RNA molecules from afirst test sample from a subject known to have a tumor, wherein thefirst test sample comprises a plurality of cell-free RNA (cfRNA)molecules; obtaining, by a computer system, a second set of sequencereads from a plurality of RNA molecules from a plurality of blood cellsfrom the subject; and detecting, by a computer system, one or more RNAsequences that are present in the first set of sequence reads, and thatare not present in the second set of sequence reads, to detect the oneor more tumor-derived RNA molecules in the subject.

In some embodiments, the first test sample obtained from the patientcomprises blood, plasma, serum, urine, saliva, pleural fluid,pericardial fluid, cerebrospinal fluid (CSF), peritoneal fluid, or anycombination thereof. In one preferred embodiment, a test sample obtainedfrom the patient is a plasma sample. In some embodiments, the pluralityof blood cells obtained from the subject are white blood cells (WBCs).

FIG. 3 is a flow diagram illustrating a method for identifying one ormore tumor-derived RNA sequences, in accordance with one embodiment ofthe present invention. At step 310, a first set of sequence reads isobtained from a biological test sample comprising a plurality ofcell-free RNA (cfRNA) molecules. In accordance with this embodiment, thecfRNA biological test sample is obtained from a test subject known tohave, or suspected of having a disease, the cfRNA molecules extractedfrom the sample and sequence reads determined (as described elsewhereherein). For example, in one embodiment, a complementary DNA strand issynthesized using a reverse transcription step generating a cDNA/RNAhybrid molecule, the RNA molecule degraded, a double stranded DNAmolecule synthesized from the cDNA strand using a polymerase, asequencing library prepared, and sequence reads determined using asequencing platform. The sequencing step can be any carried out usingany known sequencing platform in the art, as previously described.Alternatively, other means for determining the sequence reads can beused, for example, array-based hybridization, probe-based in-solutionhybridization, ligation-based assays, primer extension reaction assays,can be used to detect and/or quantify sequence reads obtained from DNAmolecules (e.g., converted from RNA molecules), as one of skill in theart would readily understand.

At step 315, a second set of sequence reads is obtained from blood cells(e.g., white blood cells or buffy coat). In one embodiment, the bloodcells are obtained from the same subject and RNA molecules extractedtherefrom. In accordance with this embodiment, the RNA molecules areextracted from the blood cells, converted to DNA, a sequencing libraryprepared, and the second set of sequence reads determined (as describedelsewhere herein). In general, any known method in the art can be usedto extract and purify cell-free nucleic acids from the test sample. Forexample, cell-free nucleic acids can be extracted and purified using oneor more known commercially available protocols or kits, such as theQIAamp circulating nucleic acid kit (Qiagen).

At step 320, one or more tumor-derived RNA molecules is detected whenone or more RNA sequences are present in the first set of sequence readsand not present in the second set of sequence reads. Moreover, one ormore sequence reads (derived from RNA molecules) present in the firstset of sequence reads, and not present in the second set of sequencereads, are identified as derived from RNA molecules indicative of adisease state. For example, the first set of sequence reads can comprisesequence reads derived from cfRNA molecules from a plasma sampleobtained from a subject known to have, or suspected of having, a disease(e.g., cancer). And the second set of sequence reads can comprisesequence reads derived from RNA molecules from blood cells (e.g., whiteblood cells). By comparing, and removing, the second set of sequencereads derived from blood cells from the first set of sequence readsderived from a cell-free RNA sample, one can identify the sequence readsderived from a tumor.

Detecting a Disease State Using a Dark Channel RNA Molecules

FIG. 4 is a flow diagram illustrating a method for detecting thepresence of cancer, determining a state of cancer, monitoring cancerprogression, and/or determining cancer type in a subject, in accordancewith one embodiment of the present invention. At step 410, a biologicaltest sample is extracted from a subject. As previously described, in oneembodiment, the test sample can be a bodily fluid (e.g., blood, plasma,serum, urine, saliva, pleural fluid, pericardial fluid, cerebrospinalfluid (CSF), peritoneal fluid, or any combination thereof) comprising aplurality of cell-free RNA molecules.

At step 415, a plurality of cell-free RNA molecules are extracted fromthe test sample and a sequencing library prepared. In general, any knownmethod in the art can be used to extract and purify cell-free nucleicacids from the test sample. For example, cell-free nucleic acids (cfRNAmolecules) can be extracted and purified using one or more knowncommercially available protocols or kits, such as the QIAamp circulatingnucleic acid kit (Qiagen). After extraction, the cfRNA molecules areused to prepare a sequencing library. In one embodiment, a reversetranscription step is used to produce a plurality of cDNA/RNA hybridmolecules, the RNA strand degraded to produce a single-stranded cDNAmolecule, a second strand synthesized to produce a plurality ofdouble-stranded DNA molecules from the single-stranded cDNA moleculetemplates, and DNA adapters ligated to the plurality of double-strandedDNA molecules to generate a sequencing library. As previously described,the DNA adapters may include one or more sequencing oligonucleotides foruse in subsequent cluster generation and/or sequencing (e.g., known P5and P7 sequences for used in sequencing by synthesis (SBS) (Illumina,San Diego, Calif.)). In another embodiment, the adapter includes asample specific index sequence, such that, after library preparation,the library can be combined with one or more other libraries preparedfrom individual samples, thereby allowing for multiplex sequencing. Inanother embodiment, unique molecular identifiers (UMI) are added throughadapter ligation.

At step 420, a sequencing reaction is performed to generate a pluralityof sequence reads. In general, any method known in the art can be usedto obtain sequence data or sequence reads from the sequencing library.For example, in one embodiment, sequencing data or sequence reads fromthe sequencing library can be acquired using next generation sequencing(NGS). Next-generation sequencing methods include, for example,sequencing by synthesis technology (Illumina), pyrosequencing (454), ionsemiconductor technology (Ion Torrent sequencing), single-moleculereal-time sequencing (Pacific Biosciences), sequencing by ligation(SOLiD sequencing), and nanopore sequencing (Oxford NanoporeTechnologies). In some embodiments, sequencing is massively parallelsequencing using sequencing-by-synthesis with reversible dyeterminators. In other embodiments, sequencing is sequencing-by-ligation.In yet other embodiments, sequencing is single molecule sequencing. Instill another embodiment, sequencing is paired-end sequencing.Optionally, an amplification step can be performed prior to sequencing.

At step 425, sequence reads obtained from the cfRNA sample are filteredto generate a list of non-excluded sequence reads and the non-excludedsequence reads quantified at step 430. For example, as describedelsewhere herein, the sequence reads obtained from the cfRNA sample canbe filtered to exclude sequence known to be present in healthy cells. Inone embodiment, RNA molecules extracted from healthy cells (e.g., whiteblood cells) are sequenced deriving sequence reads that are excludedfrom the cfRNA derived sequence reads to obtain non-excluded sequencereads. In another embodiment, RNA sequencing data from a database (e.g.,a public database) can be used to filter out or exclude sequences knownto be present in healthy cells reads comprises to obtain non-excludedsequence reads.

At step 435, a disease state is detected when the quantifiednon-excluded sequence reads exceed a threshold. In certain embodiments,the threshold value is an integer that ranges from about 1 to about 10,such as about 2, 3, 4, 5, 6, 7, 8, or about 9. In some embodiments, thethreshold is a non-integer value, ranging from about 0.1 to about 0.9,such as about 0.2, 0.3, 0.4, 0.5, 0.6, 0.7 or about 0.8.

Aspects of the invention relate to methods for detecting a presence of acancer, determining a cancer stage, monitoring a cancer progression,and/or determining a cancer type in a subject known to have, orsuspected of having a cancer. In some embodiments, the methods involve:(a) obtaining a plurality of sequence reads from a plurality of cfRNAmolecules in a biological test sample from the subject; (b)quantitatively detecting the presence of one or more sequences derivedfrom one or more RNA markers in the biological test sample to determinea tumor RNA score, wherein the one or more RNA markers are selected fromthe group consisting of one or more targeted RNA molecules; and (c)detecting the presence of the cancer, determining the cancer stage,monitoring the cancer progression, and/or determining the cancer type inthe subject when the tumor RNA score exceeds a threshold value. Incertain embodiments, the threshold value is an integer that ranges fromabout 1 to about 10, such as about 2, 3, 4, 5, 6, 7, 8, or about 9. Insome embodiments, the threshold is a non-integer value, ranging fromabout 0.1 to about 0.9, such as about 0.2, 0.3, 0.4, 0.5, 0.6, 0.7 orabout 0.8.

Quantitative detection methods in accordance with embodiments of theinvention can include nucleic acid sequencing procedures, such asnext-generation sequencing. In certain embodiments, sequencing caninvolve whole transcriptome sequencing. In certain embodiments,sequencing can involve enriching a sample for one or more targeted RNAsequences of interest prior to conducting the sequencing procedure.Alternatively, other means for detecting and quantifying sequence readscan be used, for example, array-based hybridization, probe-basedin-solution hybridization, ligation-based assays, primer extensionreaction assays, can be used to determine sequence reads from DNAmolecules (e.g., converted from RNA molecules), as one of skill in theart would readily understand.

FIG. 5 is a flow diagram illustrating a method for detecting a diseasestate from one or more sequence reads derived from one or more targetedRNA molecules, in accordance with another embodiment of the presentinvention. At step 510, a biological test sample comprising a pluralityof cell-free RNA molecules is obtained. In one embodiment, thebiological test sample is a bodily fluid (e.g., a blood, plasma, serum,urine, saliva, pleural fluid, pericardial fluid, cerebrospinal fluid(CSF), peritoneal fluid sample, or any combination thereof).

At step 515, the presence of one or more nucleic acid sequence derivedfrom one or more target RNA molecules in the biological test sample aredetected, and quantified, to determine a tumor RNA score. As describedelsewhere herein, nucleic acids derived from RNA molecules can bedetected and quantified using any known means in the art. For example,in accordance with one embodiment, nucleic acids derived from RNAmolecules are detected and quantified using a sequencing procedure, suchas a next-generation sequencing platform (e.g., HiSeq or NovaSeq,Illumina, San Diego, Calif.). In other embodiments, nucleic acidsderived from RNA molecules are detected and quantified using amicroarray, reverse transcription PCR, real-time PCR, quantitativereal-time PCR, digital PCR, digital droplet PCR, digital emulsion PCR,multiplex PCR, hybrid capture, oligonucleotide ligation assays, or anycombination thereof. As described elsewhere, in one embodiment,cell-free nucleic acids (cfRNA molecules) can be extracted and purifiedusing one or more known commercially available protocols or kits, suchas the QIAamp circulating nucleic acid kit (Qiagen). After extraction,the cfRNA molecules are used to prepare a sequencing library. In oneembodiment, a reverse transcription step is used to produce a pluralityof cDNA/RNA hybrid molecules, the RNA strand degraded to produce asingle-stranded cDNA molecule, a second strand synthesized to produce aplurality of double-stranded DNA molecules from the single-stranded cDNAmolecule templates. Optionally, in one embodiment, one or more targetedRNA molecules (or DNA molecules derived therefrom) are enriched prior todetection and quantification, as described elsewhere herein.

In one embodiment, the tumor RNA score is the quantity or count oftargeted RNA molecules (or sequence reads obtained from DNA moleculesderived from the targeted RNA molecules) detected. In anotherembodiment, the tumor RNA score comprises a mean, a mode, or an averageof the total number of targeted RNA molecules (or sequence readsobtained from DNA molecules derived from the targeted RNA molecules)detected divided by the total number of genes from which RNA moleculesare targeted. In still other embodiments, the tumor RNA score isdetermined by inputting the sequence reads into a prediction model, andthe tumor RNA score output as a likelihood or probability, as describedelsewhere herein.

At step 520, the presence of cancer is detected, a state of cancerdetermined, cancer progression monitored, and/or a cancer typedetermined in a subject when the tumor RNA score exceeds a threshold.The threshold value can be an integer that ranges from about 1 to about10, such as about 2, 3, 4, 5, 6, 7, 8, or about 9. In some embodiments,the threshold is a non-integer value, ranging from about 0.1 to about0.9, such as about 0.2, 0.3, 0.4, 0.5, 0.6, 0.7 or about 0.8.Alternatively, when the tumor RNA score is output from a predictionmodel, the output can simply be a likelihood or probability indicatingthe likelihood or probability that the subject has cancer, or a cancertype.

Cancer Indicator Score

Aspects of the invention are directed to computer-implemented methodsfor detecting the presence of a cancer in a patient. In someembodiments, the methods involve: receiving a data set in a computercomprising a processor and a computer-readable medium, wherein the dataset comprises a plurality of sequence reads obtained by sequencing aplurality of nucleic acid molecules (e.g., DNA molecules) derived from aplurality of targeted ribonucleic acid (RNA) molecules in a biologicaltest sample from the patient, and wherein the computer-readable mediumcomprises instructions that, when executed by the processor, cause thecomputer to: determine an expression level for the plurality of targetedRNA molecules from the biological test sample; comparing the expressionlevel of each of the targeted RNA molecules to an RNA tissue scorematrix to determine a cancer indicator score for each targeted RNAmolecule; aggregate the cancer indicator score for each targeted RNAmolecule to generate a cancer indicator score for the biological testsample; and detecting the presence of the cancer in the patient when thecancer indicator score for the biological test sample exceeds athreshold value.

In some embodiments, the target RNA molecules have an expression levelin patients with a known cancer status that exceeds their expressionlevel in healthy patients. In certain embodiments, an expression levelof a target RNA molecule in a patient with a known cancer status rangesfrom about 2 to about 10 times greater, such as about 3, 4, 5, 6, 7, 8,or about 9 times greater, than the expression level of the target RNAmolecule in a healthy patient. In certain embodiments, a target RNAmolecule is not detectable in a biological test sample from a healthypatient, i.e., the target RNA molecule has an undetectable expressionlevel.

In some embodiments, the number of target RNA molecules in thebiological test sample ranges from about 1 to about 2000, from about 10to about 1000, from about 10 to about 500, or from about 10 to about500. In other embodiments, the number of target RNA molecules rangesfrom about 1 to about 50, from about 1 to about 40, from about 1 toabout 30, or from about 1 to about 20, such as about 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or about 20.

In some embodiments, the cancer indicator score comprises an aggregateof the total number of targeted RNA molecules (or sequence readsobtained from DNA molecules derived from the targeted RNA molecules)detected from the biological test sample. In another embodiment, thecancer indicator score comprises a mean, a mode, or an average of thetotal number of targeted RNA molecules (or sequence reads obtained fromDNA molecules derived from the targeted RNA molecules) detected dividedby the total number of genes from which RNA molecules are targeted. Instill other embodiments, the cancer indicator score is determined byinputting the sequence reads into a prediction model, and the cancerindicator score output as a likelihood or probability, as describedelsewhere herein.

In some embodiments, the threshold value is an integer that ranges fromabout 1 to about 10, such as about 2, 3, 4, 5, 6, 7, 8, or about 9. Insome embodiments, the threshold is a non-integer value, ranging fromabout 0.1 to about 0.9, such as about 0.2, 0.3, 0.4, 0.5, 0.6, 0.7 orabout 0.8. In other embodiments, the threshold value ranges from about0.5 to about 5 reads per million (RPM), such as about 1, 1.5, 2, 2.5, 3,3.5, 4, or about 4.5 RPM. The cancer locator score threshold value canbe determined based on the quantity of targeted RNA molecules (orsequence reads derived therefrom) detected in a control sample, forexample a healthy subject or a subject with a known disease state.Alternatively, when the cancer locator score is output from a predictionmodel, the output can simply be a likelihood or probability indicatingthe likelihood or probability that the subject has cancer, or a cancertype.

FIG. 6 is a flow diagram illustrating a method for detecting thepresence of cancer in a subject based on a cancer indicator score, inaccordance with one embodiment of the present invention. At step 610, adata set is received comprising a plurality of sequence reads derivedfrom a plurality of cfRNA molecules in a biological test sample. Forexample, a plurality of sequence reads can be determined for a pluralityof cfRNA molecules extracted from a biological test sample, as describedherein. Moreover, cfRNA molecules are reverse transcribed to create DNAmolecules and the DNA molecules sequenced to generate sequence reads.

At step 615, an expression level is determined for a plurality of targetRNA molecules in the biological test sample. For example, in oneembodiment, the expression level of targeted RNA molecules can bedetermined based on quantification of detected sequence reads derivedfrom a targeted RNA molecules of interest.

At step 620, the expression level of each of the target RNA molecules iscompared to an RNA tissue score matrix to determine a cancer indicatorscore for each target RNA molecule. The RNA tissue score matrix can bedetermined from a training set comprising sequence reads derived from aplurality of cancer training samples with known cancer status.

At step 625, the cancer indicator scores for each target RNA moleculeare aggregated to generate a cancer indicator score. In someembodiments, the cancer indicator score comprises an aggregate of thetotal number of targeted RNA molecules (or sequence reads obtained fromDNA molecules derived from the targeted RNA molecules) detected from thebiological test sample. In another embodiment, the cancer indicatorscore comprises a mean, a mode, or an average of the total number oftargeted RNA molecules (or sequence reads obtained from DNA moleculesderived from the targeted RNA molecules) detected divided by the totalnumber of genes from which RNA molecules are targeted.

At step 630, detect the presence of cancer in a subject when the cancerindicator score for the test sample exceeds a threshold. As describedabove, in one embodiment, the threshold value is an integer that rangesfrom about 1 to about 10, such as about 2, 3, 4, 5, 6, 7, 8, or about 9.In some embodiments, the threshold is a non-integer value, ranging fromabout 0.1 to about 0.9, such as about 0.2, 0.3, 0.4, 0.5, 0.6, 0.7 orabout 0.8. In other embodiments, the threshold value ranges from about0.5 to about 5 reads per million (RPM), such as about 1, 1.5, 2, 2.5, 3,3.5, 4, or about 4.5 RPM.

Aspects of the invention include methods for determining a cancer celltype or tissue of origin of the cancer in the patient based on theexpression level of one or more of the target RNA molecules, the cancerindicator score for one or more of the target RNA molecules, the cancerindicator score for the biological test sample, or any combinationthereof. In certain embodiments, the methods further involvetherapeutically classifying a patient into one or more of a plurality oftreatment categories based on the expression level of one or more of thetarget RNA molecules, the cancer indicator score for one or more of thetarget RNA molecules, the cancer indicator score for the biological testsample, or any combination thereof.

In certain embodiments, the computer is configured to generate a reportthat includes an expression level of one or more of the target RNAmolecules, a cancer indicator score for one or more of the target RNAmolecules, a cancer indicator score for the biological test sample, anindication of the presence or absence of the cancer in the patient, anindication of the cancer cell type of tissue of origin of the cancer inthe patient, a therapeutic classification for the patient, or anycombination thereof.

Liver Disease

In other aspects, the present invention is directed to methods that canbe used for the assessment of NAFLD (non-alcoholic fatty liver disease)and those conditions associated with NAFLD, including fatty liverdisease, alcoholic steatohepatitis (ASH), nonalcoholic steatohepatitis(NASH), fibrosis and cirrhosis through the identification of one or moresignals indicative of, or informative for, NAFLD, ASH, NASH, fibrosis orcirrhosis in a test sample. In another embodiment, the methods disclosedherein can be used for the detection, screening, diagnosis, and/ormonitoring of hepatocellular carcinoma (HCC). The term “NAFLD”(non-alcohol fatty liver disease) refers to a group of conditions wherethere is an accumulation of excess fat in the liver of people who drinklittle or no alcohol. The most common form of NAFLD is a conditioncalled fatty liver disease. In fatty liver disease, fat accumulates inthe liver cells. A small group of people with NAFLD may have a moreserious condition termed non-alcoholic steatohepatitis (NASH). In NASH,fat accumulation is associated with liver cell inflammation anddifferent degrees of scarring. Cirrhosis occurs when the liver sustainssubstantial damage, and the liver cells are gradually replaced by scartissue which results in the inability of the liver to work properly. Theuse of the term “NAFLD” is used to include all conditions reflecting aform of non-alcohol fatty liver disease, including, NASH.

FIG. 7 is a flow diagram illustrating a method for detecting a presenceof a liver disease, determining a stage of a liver disease, and/ormonitoring progression of a liver disease in a subject, in accordancewith one embodiment of the present invention. At step 710, a biologicaltest sample comprising a plurality of cell-free RNA molecules isobtained. In one embodiment, the biological test sample is a bodilyfluid (e.g., a blood, plasma, serum, urine, saliva, pleural fluid,pericardial fluid, cerebrospinal fluid (CSF), peritoneal fluid sample,or any combination thereof).

At step 715, the presence of one or more nucleic acid sequences derivedfrom one or more target RNA molecules in the biological test sample aredetected, and quantified, to determine a liver disease RNA score. Asdescribed elsewhere herein, nucleic acids derived from RNA molecules canbe detected and quantified using any known means in the art. Forexample, in accordance with one embodiment, nucleic acids derived fromRNA molecules are detected and quantified using a sequencing procedure,such as a next-generation sequencing platform (e.g., HiSeq or NovaSeq,Illumina, San Diego, Calif.). In other embodiments, nucleic acidsderived from RNA molecules are detected and quantified using amicroarray, reverse transcription PCR, real-time PCR, quantitativereal-time PCR, digital PCR, digital droplet PCR, digital emulsion PCR,multiplex PCR, hybrid capture, oligonucleotide ligation assays, or anycombination thereof. As described elsewhere, in one embodiment,cell-free nucleic acids (cfRNA molecules) can be extracted and purifiedusing one or more known commercially available protocols or kits, suchas the QIAamp circulating nucleic acid kit (Qiagen). After extraction,the cfRNA molecules are used to prepare a sequencing library. In oneembodiment, a reverse transcription step is used to produce a pluralityof cDNA/RNA hybrid molecules, the RNA strand degraded to produce asingle-stranded cDNA molecule, a second strand synthesized to produce aplurality of double-stranded DNA molecules from the single-stranded cDNAmolecule templates.

In one embodiment, the liver disease RNA score is the quantity or countof targeted RNA molecules (or sequence reads obtained from DNA moleculesderived from the targeted RNA molecules) detected. In anotherembodiment, the liver disease RNA score comprises a mean, a mode, or anaverage of the total number of targeted RNA molecules (or sequence readsobtained from DNA molecules derived from the targeted RNA molecules)detected divided by the total number of genes from which RNA moleculesare targeted. In still other embodiments, the liver disease RNA score isdetermined by inputting the sequence reads into a prediction model, andthe liver disease RNA score output as a likelihood or probability, asdescribed elsewhere herein.

At step 720, the presence of a liver disease is detected, a stage ofliver disease determined, and/or liver disease progression monitored ina subject when the liver disease RNA score exceeds a threshold. Thethreshold value can be an integer that ranges from about 1 to about 10,such as about 2, 3, 4, 5, 6, 7, 8, or about 9. In some embodiments, thethreshold is a non-integer value, ranging from about 0.1 to about 0.9,such as about 0.2, 0.3, 0.4, 0.5, 0.6, 0.7 or about 0.8. Alternatively,when the liver disease RNA score is output from a prediction model, theoutput can simply be a likelihood or probability indicating thelikelihood or probability that the subject has cancer, or a cancer type.

Liver Disease Indicator Score

Aspects of the invention are directed to computer-implemented methodsfor detecting the presence of a liver disease in a patient. In someembodiments, the methods involve: receiving a data set in a computercomprising a processor and a computer-readable medium, wherein the dataset comprises a plurality of sequence reads obtained by sequencing aplurality of ribonucleic acid (RNA) molecules in a biological testsample from the patient, and wherein the computer-readable mediumcomprises instructions that, when executed by the processor, cause thecomputer to: determine an expression level of a plurality of target RNAmolecules in the biological test sample; compare the expression level ofeach of the target RNA molecules to an RNA tissue score matrix todetermine a liver disease indicator score for each target RNA molecule;aggregate the liver disease indicator score for each target RNA moleculeto generate a liver disease indicator score for the biological testsample; and detect the presence of the liver disease in the patient whenthe liver disease indicator score for the biological test sample exceedsa threshold value. In some embodiments, the methods involve detectingthe presence or absence of, determining the stage of, monitoring theprogression of a liver disease selected from the group consisting ofnon-alcoholic fatty liver disease (NAFLD), alcoholic steatohepatitis(ASH), nonalcoholic steatohepatitis (NASH), fibrosis and cirrhosis. Inother embodiments, the methods involve detecting the presence or absenceof, determining the stage of, monitoring the progression ofhepatocellular carcinoma (HCC).

In some embodiments, the target RNA molecules have an expression levelin patients with a known liver disease status that exceeds theirexpression level in healthy patients. In certain embodiments, anexpression level of a target RNA molecule in a patient with a knownliver disease status ranges from about 2 to about 10 times greater, suchas about 3, 4, 5, 6, 7, 8, or about 9 times greater, than the expressionlevel of the target RNA molecule in a healthy patient. In certainembodiments, a target RNA molecule is not detectable in a biologicaltest sample from a healthy patient, i.e., the target RNA molecule has anundetectable expression level.

In some embodiments, the number of target RNA molecules in thebiological test sample ranges from about 1 to about 20, such as about 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 or about 19. Inother embodiments, the threshold value ranges from about 0.5 to about 5reads per million (RPM), such as about 1, 1.5, 2, 2.5, 3, 3.5, 4, orabout 4.5 RPM.

FIG. 8 is a flow diagram illustrating a method for detecting thepresence of a liver disease in a subject based on a liver diseaseindicator score, in accordance with one embodiment of the presentinvention. At step 810, a data set is received comprising a plurality ofsequence reads derived from a plurality of cfRNA molecules in abiological test sample. For example, a plurality of sequence reads canbe determined for a plurality of cfRNA molecules extracted from abiological test sample, as described herein.

At step 815, an expression level is determined for of a plurality oftarget RNA molecules in the biological test sample. For example, in oneembodiment, the expression level of targeted RNA molecules can bedetermined based on quantification of detected sequence reads derivedfrom a targeted RNA molecules of interest, or that align to a targetedRNA molecule of interest.

At step 820, the expression level of each of the target RNA molecules iscompared to an RNA tissue score matrix to determine a liver diseaseindicator score for each target RNA molecule. The RNA tissue scorematrix can be determined from a training set comprising sequence readsderived from a plurality of liver disease training samples with knownliver disease status.

At step 825, the liver disease indicator scores for each target RNAmolecule are aggregated to generate a liver disease indicator score. Insome embodiments, the liver disease indicator score comprises anaggregate of the total number of targeted RNA molecules (or sequencereads obtained from DNA molecules derived from the targeted RNAmolecules) detected from the biological test sample. In anotherembodiment, the liver disease indicator score comprises a mean, a mode,or an average of the total number of targeted RNA molecules (or sequencereads obtained from DNA molecules derived from the targeted RNAmolecules) detected divided by the total number of genes from which RNAmolecules are targeted. In still other embodiments, the liver diseaseindicator score is determined by inputting the sequence reads into aprediction model, and the liver disease indicator score output as alikelihood or probability, as described elsewhere herein.

At step 830, detect the presence of a liver disease in a subject whenthe liver disease indicator score for the test sample exceeds athreshold. As described above, in one embodiment, the threshold value isan integer that ranges from about 1 to about 10, such as about 2, 3, 4,5, 6, 7, 8, or about 9. In some embodiments, the threshold is anon-integer value, ranging from about 0.1 to about 0.9, such as about0.2, 0.3, 0.4, 0.5, 0.6, 0.7 or about 0.8. In other embodiments, thethreshold value ranges from about 0.5 to about 5 reads per million(RPM), such as about 1, 1.5, 2, 2.5, 3, 3.5, 4, or about 4.5 RPM.Alternatively, when the liver disease indicator score is output from aprediction model, the output can simply be a likelihood or probabilityindicating the likelihood or probability that the subject has cancer, ora cancer type

RNA Tissue Matrix Score

Aspects of the invention include methods for constructing an RNA tissuescore matrix. In some embodiments, the methods involve compiling aplurality of RNA sequence reads obtained from a plurality of patients togenerate an RNA expression matrix, and normalizing the RNA expressionmatrix with a tissue-specific RNA expression matrix to construct the RNAtissue score matrix. In certain embodiments, the tissue-specific RNAexpression matrix comprises a plurality of reference human tissues. Incertain embodiments, the RNA sequence reads are obtained from aplurality of healthy patients to construct a healthy RNA tissue scorematrix. In certain embodiments, the RNA sequence reads are obtained froma plurality of patients having a known cancer type to construct a cancerRNA tissue score matrix.

RNA Markers and Analysis Technique

Methods in accordance with some embodiments of the invention can beperformed on cfRNA molecules and/or ctRNA molecules. In someembodiments, RNA molecules that are used in the subject methods includeRNA molecules from cancerous and non-cancerous cells.

In embodiments, methods include: (a) measuring a plurality of targetcell-free RNA (cfRNA) molecules in a sample of the subject, wherein theplurality of target cfRNA molecules are selected from transcripts ofTables 1-7; and (b) detecting the cancer, wherein detecting the cancercomprises detecting one or more of the target cfRNA molecules above athreshold level. In embodiments, the plurality of target cfRNA moleculesare selected from at least 2, 3, 4, 5, 10, 15, 20, 25, or moretranscripts of genes listed in one or more of Tables 1-7. Target cfRNAmolecules can be from genes selected from any one of these tables, orany combination thereof. In embodiments, the number of tables selectedfrom among Tables 1-7 is 2, 3, 4, 5, or all tables. In embodiments, thetarget cfRNA molecules that are measured are from fewer than 500 genes(e.g., fewer than 400, 300, 200, 100, or 50 genes).

In some embodiments, one or more target cfRNA molecules are derived fromone or more genes selected from the genes listed in Table 1. Inembodiments, the one or more target cfRNA molecules includes at least 2,3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, or 30 genes from Table 1. Inembodiments, the one or more target cfRNA molecules includes at least 5genes from Table 1. In embodiments, the one or more target cfRNAmolecules includes at least 10 genes from Table 1. In embodiments, theone or more target cfRNA molecules includes all of the genes fromTable 1. In embodiments, the one or more target cfRNA molecules includesat least one of the first 5 genes of Table 1 (AGR2, HOXC10, S100A7,BPIFA1, and IDI2-AS1), and optionally one or more additional genes fromTable 1. In embodiments, the one or more target cfRNA molecules includestranscripts of the AGR2 gene. In embodiments, the one or more targetcfRNA molecules includes transcripts of AGR2, HOXC10, S100A7, BPIFA1,and IDI2-AS1. In embodiments, the target cfRNA molecules that aremeasured are from fewer than 500 genes (e.g., fewer than 400, 300, 200,100, or 50 genes). Table 1 below provides examples of cancer darkchannel biomarkers.

TABLE 1 AGR2 HOXC10 S100A7 BPIFA1 IDI2-AS1 SCGB2A2 CASP14 KRT16P2SERPINB5 CSN1S1 LALBA SFTA3 DISP2 LINC00163 SFTPA2 EIF2D NKX2-1 SLC34A2FABP7 OPN1SW TFF1 GABRG1 PADI3 VTCN1 GNAT3 PTPRZ1 WFDC2 GRHL2 ROS1 MUC5BSMIM22 CXCL17 RNU1-1 KLK5

In some embodiments, one or more target cfRNA molecules are derived fromone or more genes selected from the genes listed in Table 2. Inembodiments, the one or more target cfRNA molecules includes at least 2,3, 4, 5, 6, 7, 8, 9, or 10 genes from Table 2. In embodiments, the oneor more target cfRNA molecules includes at least 5 genes from Table 2.In embodiments, the one or more target cfRNA molecules includes at least10 genes from Table 2. In embodiments, the one or more target cfRNAmolecules includes all of the genes from Table 2. In embodiments, theone or more target cfRNA molecules include at least one of the first 5genes of Table 2 (ROS1, NKX2-1, GGTLC1, SLC34A2, and SFTPA2), andoptionally one or more additional genes from Table 2. In embodiments,the one or more target cfRNA molecules include transcripts of the ROS1gene. In embodiments, the one or more target cfRNA molecules includetranscripts of ROS1, NKX2-1, GGTLC1, SLC34A2, and SFTPA2. Inembodiments, the target cfRNA molecules that are measured are from fewerthan 500 genes (e.g., fewer than 400, 300, 200, 100, or 50 genes). Table2 below provides examples of dark channel lung cancer biomarkers.

TABLE 2 ROS1 NKX2-1 GGTLC1 SLC34A2 SFTPA2 BPIFA1 SFTA3 GABRG1 AGR2 GNAT3MUC5B SMIM22 CXCL17 WFDC2

In some embodiments, one or more target cfRNA molecules are derived fromone or more genes selected from the genes listed in Table 3. Inembodiments, the one or more target cfRNA molecules includes at least 2,3, 4, 5, 6, 7, 8, 9, or 10 genes from Table 3. In embodiments, the oneor more target cfRNA molecules includes at least 5 genes from Table 3.In embodiments, the one or more target cfRNA molecules includes all ofthe genes from Table 3. In embodiments, the one or more target cfRNAmolecules include at least one of the first 5 genes of Table 3 (SCGB2A2,CSN1S1, VTCN1, FABP7, and LALBA), and optionally one or more additionalgenes from Table 3. In embodiments, the one or more target cfRNAmolecules include transcripts of the SCGB2A2 gene. In embodiments, theone or more target cfRNA molecules include transcripts of SCGB2A2,CSN1S1, VTCN1, FABP7, and LALBA. In embodiments, the target cfRNAmolecules that are measured are from fewer than 500 genes (e.g., fewerthan 400, 300, 200, 100, or 50 genes). Table 3 below provides examplesof breast cancer dark channel biomarkers.

TABLE 3 SCGB2A2 CSN1S1 VTCN1 FABP7 LALBA RNU1-1 CASP14 KLK5 WFDC2 OPN1SW

In some embodiments, one or more target cfRNA molecules are derived fromone or more genes selected from the genes listed in Table 4. Inembodiments, the one or more target cfRNA molecules includes at least 2,3, 4, or 5 genes from Table 4. In embodiments, the one or more targetcfRNA molecules includes at least 5 genes from Table 4. In embodiments,the one or more target cfRNA molecules includes all of the genes fromTable 4. In embodiments, the one or more target cfRNA molecules includeat least one of the first 5 genes of Table 4 (CASP14, CRABP2, FABP7,SCGB2A2, and SERPINB5), and optionally one or more additional genes fromTable 4. In embodiments, the one or more target cfRNA molecules includetranscripts of the CASP14 gene. In embodiments, the one or more targetcfRNA molecules include transcripts of CASP14, CRABP2, FABP7, SCGB2A2,and SERPINB5. In embodiments, the target cfRNA molecules that aremeasured are from fewer than 500 genes (e.g., fewer than 400, 300, 200,100, or 50 genes). Table 4 below provides examples of breast cancerbiomarkers identified using a heteroDE method, as described herein.

TABLE 4 CASP14 CRABP2 FABP7 SCGB2A2 SERPINB5 TRGV10 VGLL1 TFF1AC007563.5

In some embodiments, one or more target cfRNA molecules are derived fromone or more genes selected from the genes listed in Table 5. Inembodiments, the one or more target cfRNA molecules includes at least 2,3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or 25 genes from Table 5. Inembodiments, the one or more target cfRNA molecules includes at least 5genes from Table 5. In embodiments, the one or more target cfRNAmolecules includes at least 10 genes from Table 5. In embodiments, theone or more target cfRNA molecules includes all of the genes from Table5. In embodiments, the one or more target cfRNA molecules include atleast one of the first 5 genes of Table 5 (PTPRZ1, AGR2, SHANK1, PON1,and MYO16_AS1), and optionally one or more additional genes from Table5. In embodiments, the one or more target cfRNA molecules includetranscripts of the PTPRZ1 gene. In embodiments, the one or more targetcfRNA molecules include transcripts of PTPRZ1, AGR2, SHANK1, PON1, andMYO16_AS1. In embodiments, the target cfRNA molecules that are measuredare from fewer than 500 genes (e.g., fewer than 400, 300, 200, 100, or50 genes). Table 5 below provides examples of lung cancer biomarkersidentified using an information gain method, as described herein.

TABLE 5 PTPRZ1 AGR2 SHANK1 PON1 MYO16_AS1 NPAS3 LINC00407 LMO3 KRT15ELFN2 MUC5B SAA2 SLIT3 NALCN LUM GDA LINC01498 TMEM178A RCVRN XKRX ROS1NBPF7 ACSM5 SLC10A3 SAA1 CYP3A4 LINC00643 GLP1R TRAV8_5 GNAT3

In some embodiments, one or more target cfRNA molecules are derived fromone or more genes selected from the genes listed in Table 6. Inembodiments, the one or more target cfRNA molecules includes at least 2,3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or 25 genes from Table 6. Inembodiments, the one or more target cfRNA molecules includes at least 5genes from Table 6. In embodiments, the one or more target cfRNAmolecules includes at least 10 genes from Table 6. In embodiments, theone or more target cfRNA molecules includes all of the genes from Table6. In embodiments, the one or more target cfRNA molecules include atleast one of the first 5 genes of Table 6 (ADARB2, HORMAD2, SPDYE18,RPS19, and CYP4F35P), and optionally one or more additional genes fromTable 6. In embodiments, the one or more target cfRNA molecules includetranscripts of the ADARB2 gene. In embodiments, the one or more targetcfRNA molecules include transcripts of ADARB2, HORMAD2, SPDYE18, RPS19,and CYP4F35P. In embodiments, the target cfRNA molecules that aremeasured are from fewer than 500 genes (e.g., fewer than 400, 300, 200,100, or 50 genes). Table 6 below provides examples of breast cancerbiomarkers identified using an information gain method, as describedherein.

TABLE 6 ADARB2 HORMAD2 SPDYE18 RPS19 CYP4F35P MIR503HG SLC34A2 MUC5BIGKVID_16 TLX2 IDI2 PDPK2P ACTBP2 TTPA LINC01140 RIMKLA WNT6 TRBV6_4RANBP6 FHOD3 LINC00856 CTF1 GSTA9P FOXC1 FAM9C SMIM2_AS1 CCDC188FAM171A2 GRIA2 GABRR2

In some embodiments, one or more target cfRNA molecules are derived fromone or more genes selected from the genes listed in Table 7. Inembodiments, the one or more target cfRNA molecules includes at least 2,3, 4, 5, 6, 7, 8, 9, or 10 genes from Table 7. In embodiments, the oneor more target cfRNA molecules includes at least 5 genes from Table 7.In embodiments, the one or more target cfRNA molecules includes at least10 genes from Table 7. In embodiments, the one or more target cfRNAmolecules includes all of the genes from Table 7. In embodiments, theone or more target cfRNA molecules include at least one of the first 5genes of Table 7 (S100A7, FOXA1, BARX2, MMP7, and PLEKHG4B), andoptionally one or more additional genes from Table 7. In embodiments,the one or more target cfRNA molecules include transcripts of the S100A7gene. In embodiments, the one or more target cfRNA molecules includetranscripts of S100A7, FOXA1, BARX2, MMP7, and PLEKHG4B. In embodiments,the target cfRNA molecules that are measured are from fewer than 500genes (e.g., fewer than 400, 300, 200, 100, or 50 genes). Table 7 belowprovides examples of dark channel cancer biomarkers that are expressedat relatively high levels in cancer tissue.

TABLE 7 S100A7 FOXA1 BARX2 MMP7 PLEKHG4B TFAP2A TOX3 VTCN1 ANKRD30ACOL22A1 FDCSP LAMA1 MATN3 TFF1 VGLL1

In some embodiments, one or more target cfRNA molecules are derived fromone or more genes selected from Table 1 (e.g., 2, 3, 5, or more genes)in combination with (a) one or more genes selected from Table 5 or Table6 (e.g., 2, 3, 5, or more genes), and/or (b) one or more genes selectedfrom Table 7 (e.g., 2, 3, 5, or more genes). In embodiments, selectionof genes from first and second tables comprises selecting one or moregenes in both of the first and second tables. In embodiments, selectionof genes from first and second tables comprises selecting one or moregenes from the first table that are not in the second, and one or moregenes from the second table that are not in the first.

In embodiments, one or more target cfRNA molecules are derived from oneor more genes selected from Table 2 (e.g., 2, 3, 5, or more genes) incombination with (a) one or more genes selected from Table 5 (e.g., 2,3, 5, or more genes), and/or (b) one or more genes selected from Table 7(e.g., 2, 3, 5, or more genes). In embodiments, selection of genes fromfirst and second tables comprises selecting one or more genes in both ofthe first and second tables. In embodiments, selection of genes fromfirst and second tables comprises selecting one or more genes from thefirst table that are not in the second, and one or more genes from thesecond table that are not in the first.

In embodiments, one or more target cfRNA molecules are derived from oneor more genes selected from Table 3 (e.g., 2, 3, 5, or more genes) incombination with (a) one or more genes selected from Table 4 (e.g., 2,3, 5 or more genes), (b) one or more genes selected from Table 6 (e.g.,2, 3, 5, or more genes), and/or (c) one or more genes selected fromTable 7 (e.g., 2, 3, 5, or more genes). In embodiments, selection ofgenes from first and second tables comprises selecting one or more genesin both of the first and second tables. In embodiments, selection ofgenes from first and second tables comprises selecting one or more genesfrom the first table that are not in the second, and one or more genesfrom the second table that are not in the first.

In embodiments, one or more target cfRNA molecules are derived from oneor more genes selected from Table 4 (e.g., 2, 3, 5, or more genes) incombination with (a) one or more genes selected from Table 3 (e.g., 2,3, 5, or more genes), (b) one or more genes selected from Table 6 (e.g.,2, 3, 5, or more genes, and/or (c) one or more genes selected from Table7 (e.g., 2, 3, 5, or more genes). In embodiments, selection of genesfrom first and second tables comprises selecting one or more genes inboth of the first and second tables. In embodiments, selection of genesfrom first and second tables comprises selecting one or more genes fromthe first table that are not in the second, and one or more genes fromthe second table that are not in the first.

In some embodiments, one or more target cfRNA molecules are derived fromTable 8. In embodiments, the one or more target cfRNA molecules includesa plurality of genes from Table 8. In embodiments, the one or moretarget cfRNA molecules includes all of the genes from Table 8. Inembodiments, the one or more target cfRNA molecules include transcriptsof the AKR1B10 gene. In embodiments, the target cfRNA molecules that aremeasured are from fewer than 500 genes (e.g., fewer than 400, 300, 200,100, or 50 genes). Table 7 below provides examples of liver disease darkchannel biomarkers.

TABLE 8 AKR1B10 C3 PIEXO2

Diseases and Disorders

Methods in accordance with embodiments of the invention can be used fordetecting the presence or absence of any of a variety of diseases orconditions, including, but not limited to, cardiovascular disease, liverdisease, or cancer. In some embodiments, the methods involve determininga cancer stage. In some embodiments, the cancer stage is stage I cancer,stage II cancer, stage III cancer, or stage IV cancer.

In some embodiments, the methods involve detecting the presence orabsence of, determining the stage of, monitoring the progression of,and/or classifying a carcinoma, a sarcoma, a myeloma, a leukemia, alymphoma, a blastoma, a germ cell tumor, or any combination thereof. Insome embodiments, the carcinoma may be an adenocarcinoma. In otherembodiments, the carcinoma may be a squamous cell carcinoma. In stillother embodiments, the carcinoma is selected from the group consistingof small cell lung cancer, non-small-cell lung, nasopharyngeal,colorectal, anal, liver, urinary bladder, cervical, testicular, ovarian,gastric, esophageal, head-and-neck, pancreatic, prostate, renal,thyroid, melanoma, and breast carcinoma. In some embodiments, the breastcarcinoma is hormone receptor negative breast carcinoma or triplenegative breast carcinoma.

In some embodiments, the methods involve detecting the presence orabsence of, determining the stage of, monitoring the progression of,and/or classifying a sarcoma. In certain embodiments, the sarcoma can beselected from the group consisting of osteosarcoma, chondrosarcoma,leiomyosarcoma, rhabdomyosarcoma, mesothelial sarcoma (mesothelioma),fibrosarcoma, angiosarcoma, liposarcoma, glioma, and astrocytoma. Instill other embodiments, the methods involve detecting the presence orabsence of, determining the stage of, monitoring the progression of,and/or classifying leukemia. In certain embodiments, the leukemia can beselected from the group consisting of: myelogenous, granulocytic,lymphatic, lymphocytic, and lymphoblastic leukemia. In still otherembodiments, the methods involve detecting the presence or absence of,determining the stage of, monitoring the progression of, and/orclassifying a lymphoma. In certain embodiments, the lymphoma can beselected from the group consisting of: Hodgkin's lymphoma andNon-Hodgkin's lymphoma.

Aspects of the invention include methods for determining a tissue oforigin of a disease, wherein the tissue of origin is selected from thegroup consisting of pancreatic tissue, hepatobiliary tissue, livertissue, lung tissue, brain tissue, neuroendocrine tissue, uterus tissue,renal tissue, urothelial tissue, renal tissue, cervical tissue, breasttissue, fat, colon tissue, rectum tissue, heart tissue, skeletal muscletissue, prostate tissue and thyroid tissue.

Aspects of the invention include methods for determining a cancer celltype, wherein the cancer cell type is selected from the group consistingof bladder cancer, breast cancer, cervical cancer, colorectal cancer,endometrial cancer, esophageal cancer, gastric cancer, head/neck cancer,hepatobiliary cancer, hematological cancer, liver cancer, lung cancer, alymphoma, a melanoma, multiple myeloma, ovarian cancer, pancreaticcancer, prostate cancer, renal cancer, thyroid cancer, urethral cancerand uterine cancer.

Treating Conditions

Methods disclosed herein can be used in making therapeutic decisions,guidance and monitoring, as well as development and clinical trials ofcancer therapies. For example, treatment efficacy can be monitored bycomparing patient cfRNA in samples from before, during, and aftertreatment with particular therapies such as molecular targeted therapies(monoclonal drugs), chemotherapeutic drugs, radiation protocols, etc. orcombinations of these. In some embodiments, cfRNA is monitored to see ifcertain cancer biomarkers increase or decrease after treatment, whichcan allow a physician to alter a treatment (continue, stop or changetreatment, for example) in a much shorter period of time than affordedby methods of monitoring that track traditional patient symptoms. Insome embodiments, a method further comprises the step of diagnosing asubject based on the RNA-derived sequences, such as diagnosing thesubject with a particular stage or type of cancer associated with adetected cfRNA biomarker, or reporting a likelihood that the patient hasor will develop such cancer. In embodiments, methods disclosed hereinfurther comprise selecting a treatment based on the condition detected.In embodiments, the selected treatment is administered to the subject.Where the condition is cancer, or a particular cancer type and/or stage,an appropriate anti-cancer therapy may be selected. Non-limitingexamples of anti-cancer therapies include radiation therapy, surgicalresection, administration of an anti-cancer agent (e.g., animmunotherapy agent, a chemotherapy agent, or the like), or acombination of one or more of these.

Classification Model

Aspects of the invention are directed to classification models. Forexample, a machine learning or deep learning model (e.g., a diseaseclassifier) can be used to determine a disease state based on values ofone or more features determined from one or more RNA molecules orsequence reads (derived from one or more cfRNA molecules). In variousembodiments, the output of the machine learning or deep learning modelis a predictive score or probability of a disease state (e.g., apredictive cancer score). Therefore, the machine learning or deeplearning model generates a disease state classification based on thepredictive score or probability.

In some embodiments, the machine learned model includes a logisticregression classifier. In other embodiments, the machine learning ordeep learning model can be one of a decision tree, an ensemble (e.g.,bagging, boosting, random forest), gradient boosting machine, ion, NaïveBayes, support vector machine, or a neural network. The disease statemodel includes learned weights for the features that are adjusted duringtraining. The term weights is used generically here to represent thelearned quantity associated with any given feature of a model,regardless of which particular machine learning technique is used. Insome embodiments, a cancer indicator score is determined by inputtingvalues for features derived from one or more RNA sequences (or DNAsequence reads thereof) into a machine learning or deep learning model.In other embodiments, a liver disease indicator score is determined byinputting values for features derived from one or more RNA sequences (orDNA sequence reads thereof) into a machine learning or deep learningmodel.

During training, training data is processed to generate values forfeatures that are used to train the weights of the disease state model.As an example, training data can include cfRNA data and/or WBC RNA dataobtained from training samples, as well as an output label. For example,the output label can be indication as to whether the individual is knownto have a specific disease (e.g., known to have cancer) or known to behealthy (i.e., devoid of a disease). In other embodiments, the model canbe used to determine a disease type, or tissue of origin (e.g., cancertissue of origin), or an indication of a severity of the disease (e.g.,cancer stage) and generate an output label therefor. Depending on theparticular embodiment, the disease state model receives the values forone or more of the features determine from an RNA assay used fordetection and quantification of a cfRNA molecule or sequence derivedtherefrom, and computational analyses relevant to the model to betrained. In one embodiment, the one or more features comprise a quantityof one or more cfRNA molecules or sequence reads derived therefrom.Depending on the differences between the scores output by themodel-in-training and the output labels of the training data, theweights of the predictive cancer model are optimized to enable thedisease state model to make more accurate predictions. In variousembodiments, a disease state model may be a non-parametric model (e.g.,k-nearest neighbors) and therefore, the predictive cancer model can betrained to make more accurately make predictions without having tooptimize parameters.

The trained disease state model can be stored in a computer readablemedium, and subsequently retrieved when needed, for example, duringdeployment of the model.

In some embodiments, the methods involve transforming a gene expressionmatrix (G) into a tissue score matrix (S) by multiplying the geneexpression matrix (G) with a tissue specificity matrix (TS). G_(m,n) isthe expression level for gene n in sample m. TS_(n,j) is the tissuespecificity of gene n for tissue j. If gene n is not specific for tissuej, TS_(n,j)=0. In some embodiments, the tissue specificity matrix iscalculated using the tissue RNA-seq database (GTEx). The tissue scorescan be used as features to build models to classify, e.g., cancer versusnon-cancer samples. In one non-limiting embodiment, the dark channelgenes identified from lung cancer samples (SFTPA2, SLC39A4, NKX2_1,SFTPA1, BPIFA1, SLC34A2, CXCL17, SFTA3, MUC1, AGR2, WFDC2, ABCA12,VSIG10, CRABP2) were used to build a decision tree classifier todistinguish lung cancer from non-cancer cfRNA samples. The results ofthis analysis are shown in FIG. 10 .

Sequencing and Bioinformatics

Aspects of the invention include sequencing of nucleic acid molecules togenerate a plurality of sequence reads, and bioinformatic manipulationof the sequence reads to carry out the subject methods.

In certain embodiments, a sample is collected from a subject, followedby enrichment for genetic regions or genetic fragments of interest. Forexample, in some embodiments, a sample can be enriched by hybridizationto a nucleotide array comprising cancer-related genes or gene fragmentsof interest. In some embodiments, a sample can be enriched for genes ofinterest (e.g., cancer-associated genes) using other methods known inthe art, such as hybrid capture. See, e.g., Lapidus (U.S. Pat. No.7,666,593), the contents of which is incorporated by reference herein inits entirety. In one hybrid capture method, a solution-basedhybridization method is used that includes the use of biotinylatedoligonucleotides and streptavidin coated magnetic beads. See, e.g.,Duncavage et al., J Mol Diagn. 13(3): 325-333 (2011); and Newman et al.,Nat Med. 20(5): 548-554 (2014). Isolation of nucleic acid from a samplein accordance with the methods of the invention can be done according toany method known in the art.

Sequencing may be by any method or combination of methods known in theart. For example, known nucleic acid sequencing techniques include, butare not limited to, classic dideoxy sequencing reactions (Sanger method)using labeled terminators or primers and gel separation in slab orcapillary, sequencing by synthesis using reversibly terminated labelednucleotides, pyrosequencing, 454 sequencing, allele specifichybridization to a library of labeled oligonucleotide probes, sequencingby synthesis using allele specific hybridization to a library of labeledclones that is followed by ligation, real time monitoring of theincorporation of labeled nucleotides during a polymerization step,Polony sequencing, and SOLiD sequencing. Sequencing of separatedmolecules has more recently been demonstrated by sequential or singleextension reactions using polymerases or ligases as well as by single orsequential differential hybridizations with libraries of probes.

One conventional method to perform sequencing is by chain terminationand gel separation, as described by Sanger et al., Proc Natl. Acad. Sci.USA, 74(12): 5463 67 (1977), the contents of which are incorporated byreference herein in their entirety. Another conventional sequencingmethod involves chemical degradation of nucleic acid fragments. See,Maxam et al., Proc. Natl. Acad. Sci., 74: 560 564 (1977), the contentsof which are incorporated by reference herein in their entirety. Methodshave also been developed based upon sequencing by hybridization. See,e.g., Harris et al., (U.S. patent application number 2009/0156412), thecontents of which are incorporated by reference herein in theirentirety.

A sequencing technique that can be used in the methods of the providedinvention includes, for example, Helicos True Single Molecule Sequencing(tSMS) (Harris T. D. et al. (2008) Science 320:106-109), the contents ofwhich are incorporated by reference herein in their entirety. Furtherdescription of tSMS is shown, for example, in Lapidus et al. (U.S. Pat.No. 7,169,560), the contents of which are incorporated by referenceherein in their entirety, Lapidus et al. (U.S. patent applicationpublication number 2009/0191565, the contents of which are incorporatedby reference herein in their entirety), Quake et al. (U.S. Pat. No.6,818,395, the contents of which are incorporated by reference herein intheir entirety), Harris (U.S. Pat. No. 7,282,337, the contents of whichare incorporated by reference herein in their entirety), Quake et al.(U.S. patent application publication number 2002/0164629, the contentsof which are incorporated by reference herein in their entirety), andBraslaysky, et al., PNAS (USA), 100: 3960-3964 (2003), the contents ofwhich are incorporated by reference herein in their entirety.

Another example of a nucleic acid sequencing technique that can be usedin the methods of the provided invention is 454 sequencing (Roche)(Margulies, M et al. 2005, Nature, 437, 376-380, the contents of whichare incorporated by reference herein in their entirety). Another exampleof a DNA sequencing technique that can be used in the methods of theprovided invention is SOLiD technology (Applied Biosystems). Anotherexample of a DNA sequencing technique that can be used in the methods ofthe provided invention is Ion Torrent sequencing (U.S. patentapplication publication numbers 2009/0026082, 2009/0127589,2010/0035252, 2010/0137143, 2010/0188073, 2010/0197507, 2010/0282617,2010/0300559, 2010/0300895, 2010/0301398, and 2010/0304982, the contentsof each of which are incorporated by reference herein in theirentirety).

In some embodiments, the sequencing technology is Illumina sequencing.Illumina sequencing is based on the amplification of DNA on a solidsurface using fold-back PCR and anchored primers. Genomic DNA can befragmented, or in the case of cfDNA, fragmentation is not needed due tothe already short fragments. Adapters are ligated to the 5′ and 3′ endsof the fragments. DNA fragments that are attached to the surface of flowcell channels are extended and bridge amplified. The fragments becomedouble stranded, and the double stranded molecules are denatured.Multiple cycles of the solid-phase amplification followed bydenaturation can create several million clusters of approximately 1,000copies of single-stranded DNA molecules of the same template in eachchannel of the flow cell. Primers, DNA polymerase and fourfluorophore-labeled, reversibly terminating nucleotides are used toperform sequential sequencing. After nucleotide incorporation, a laseris used to excite the fluorophores, and an image is captured and theidentity of the first base is recorded. The 3′ terminators andfluorophores from each incorporated base are removed and theincorporation, detection and identification steps are repeated.

Another example of a sequencing technology that can be used in themethods of the provided invention includes the single molecule,real-time (SMRT) technology of Pacific Biosciences. Yet another exampleof a sequencing technique that can be used in the methods of theprovided invention is nanopore sequencing (Soni G V and Meller A. (2007)Clin Chem 53: 1996-2001, the contents of which are incorporated byreference herein in their entirety). Another example of a sequencingtechnique that can be used in the methods of the provided inventioninvolves using a chemical-sensitive field effect transistor (chemFET)array to sequence DNA (for example, as described in US PatentApplication Publication No. 20090026082, the contents of which areincorporated by reference herein in their entirety). Another example ofa sequencing technique that can be used in the methods of the providedinvention involves using an electron microscope (Moudrianakis E. N. andBeer M. Proc Natl Acad Sci USA. 1965 March; 53:564-71, the contents ofwhich are incorporated by reference herein in their entirety).

If the nucleic acid from the sample is degraded or only a minimal amountof nucleic acid can be obtained from the sample, PCR can be performed onthe nucleic acid in order to obtain a sufficient amount of nucleic acidfor sequencing (See, e.g., Mullis et al. U.S. Pat. No. 4,683,195, thecontents of which are incorporated by reference herein in its entirety)

Computer Systems and Devices

Aspects of the invention described herein can be performed using anytype of computing device, such as a computer, that includes a processor,e.g., a central processing unit, or any combination of computing deviceswhere each device performs at least part of the process or method. Insome embodiments, systems and methods described herein may be performedwith a handheld device, e.g., a smart tablet, or a smart phone, or aspecialty device produced for the system.

Methods of the invention can be performed using software, hardware,firmware, hardwiring, or combinations of any of these. Featuresimplementing functions can also be physically located at variouspositions, including being distributed such that portions of functionsare implemented at different physical locations (e.g., imaging apparatusin one room and host workstation in another, or in separate buildings,for example, with wireless or wired connections).

Processors suitable for the execution of computer programs include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory, or both. The essential elements of a computerare a processor for executing instructions and one or more memorydevices for storing instructions and data. Generally, a computer willalso include, or be operatively coupled to receive data from or transferdata to, or both, one or more mass storage devices for storing data,e.g., magnetic, magneto-optical disks, or optical disks. Informationcarriers suitable for embodying computer program instructions and datainclude all forms of non-volatile memory, including, by way of example,semiconductor memory devices, (e.g., EPROM, EEPROM, solid state drive(SSD), and flash memory devices); magnetic disks, (e.g., internal harddisks or removable disks); magneto-optical disks; and optical disks(e.g., CD and DVD disks). The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, the subject matter describedherein can be implemented on a computer having an I/O device, e.g., aCRT, LCD, LED, or projection device for displaying information to theuser and an input or output device such as a keyboard and a pointingdevice, (e.g., a mouse or a trackball), by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well. For example, feedback provided to theuser can be any form of sensory feedback, (e.g., visual feedback,auditory feedback, or tactile feedback), and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The subject matter described herein can be implemented in a computingsystem that includes a back-end component (e.g., a data server), amiddleware component (e.g., an application server), or a front-endcomponent (e.g., a client computer having a graphical user interface ora web browser through which a user can interact with an implementationof the subject matter described herein), or any combination of suchback-end, middleware, and front-end components. The components of thesystem can be interconnected through a network by any form or medium ofdigital data communication, e.g., a communication network. For example,a reference set of data may be stored at a remote location and acomputer can communicate across a network to access the reference dataset for comparison purposes. In other embodiments, however, a referencedata set can be stored locally within the computer, and the computeraccesses the reference data set within the CPU for comparison purposes.Examples of communication networks include, but are not limited to, cellnetworks (e.g., 3G or 4G), a local area network (LAN), and a wide areanetwork (WAN), e.g., the Internet.

The subject matter described herein can be implemented as one or morecomputer program products, such as one or more computer programstangibly embodied in an information carrier (e.g., in a non-transitorycomputer-readable medium) for execution by, or to control the operationof, a data processing apparatus (e.g., a programmable processor, acomputer, or multiple computers). A computer program (also known as aprogram, software, software application, app, macro, or code) can bewritten in any form of programming language, including compiled orinterpreted languages (e.g., C, C++, Perl), and it can be deployed inany form, including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment.Systems and methods of the invention can include instructions written inany suitable programming language known in the art, including, withoutlimitation, C, C++, Perl, Java, ActiveX, HTML5, Visual Basic, orJavaScript.

A computer program does not necessarily correspond to a file. A programcan be stored in a file or a portion of a file that holds other programsor data, in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

A file can be a digital file, for example, stored on a hard drive, SSD,CD, or other tangible, non-transitory medium. A file can be sent fromone device to another over a network (e.g., as packets being sent from aserver to a client, for example, through a Network Interface Card,modem, wireless card, or similar).

Writing a file according to the invention involves transforming atangible, non-transitory computer-readable medium, for example, byadding, removing, or rearranging particles (e.g., with a net charge ordipole moment into patterns of magnetization by read/write heads), thepatterns then representing new collocations of information aboutobjective physical phenomena desired by, and useful to, the user. Insome embodiments, writing involves a physical transformation of materialin tangible, non-transitory computer readable media (e.g., with certainoptical properties so that optical read/write devices can then read thenew and useful collocation of information, e.g., burning a CD-ROM). Insome embodiments, writing a file includes transforming a physical flashmemory apparatus such as NAND flash memory device and storinginformation by transforming physical elements in an array of memorycells made from floating-gate transistors. Methods of writing a file arewell-known in the art and, for example, can be invoked manually orautomatically by a program or by a save command from software or a writecommand from a programming language.

Suitable computing devices typically include mass memory, at least onegraphical user interface, at least one display device, and typicallyinclude communication between devices. The mass memory illustrates atype of computer-readable media, namely computer storage media. Computerstorage media may include volatile, nonvolatile, removable, andnon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, or other data. Examples of computer storage mediainclude RAM, ROM, EEPROM, flash memory, or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, Radiofrequency Identification (RFID) tags or chips, orany other medium that can be used to store the desired information, andwhich can be accessed by a computing device.

Functions described herein can be implemented using software, hardware,firmware, hardwiring, or combinations of any of these. Any of thesoftware can be physically located at various positions, including beingdistributed such that portions of the functions are implemented atdifferent physical locations.

As one skilled in the art would recognize as necessary or best-suitedfor performance of the methods of the invention, a computer system forimplementing some or all of the described inventive methods can includeone or more processors (e.g., a central processing unit (CPU) a graphicsprocessing unit (GPU), or both), main memory and static memory, whichcommunicate with each other via a bus.

A processor will generally include a chip, such as a single core ormulti-core chip, to provide a central processing unit (CPU). A processmay be provided by a chip from Intel or AMD.

Memory can include one or more machine-readable devices on which isstored one or more sets of instructions (e.g., software) which, whenexecuted by the processor(s) of any one of the disclosed computers canaccomplish some or all of the methodologies or functions describedherein. The software may also reside, completely or at least partially,within the main memory and/or within the processor during executionthereof by the computer system. Preferably, each computer includes anon-transitory memory such as a solid state drive, flash drive, diskdrive, hard drive, etc.

While the machine-readable devices can in an exemplary embodiment be asingle medium, the term “machine-readable device” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more sets of instructions and/or data. These terms shall alsobe taken to include any medium or media that are capable of storing,encoding, or holding a set of instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present invention. These terms shall accordingly betaken to include, but not be limited to, one or more solid-statememories (e.g., subscriber identity module (SIM) card, secure digitalcard (SD card), micro SD card, or solid-state drive (SSD)), optical andmagnetic media, and/or any other tangible storage medium or media.

A computer of the invention will generally include one or more I/Odevice such as, for example, one or more of a video display unit (e.g.,a liquid crystal display (LCD) or a cathode ray tube (CRT)), analphanumeric input device (e.g., a keyboard), a cursor control device(e.g., a mouse), a disk drive unit, a signal generation device (e.g., aspeaker), a touchscreen, an accelerometer, a microphone, a cellularradio frequency antenna, and a network interface device, which can be,for example, a network interface card (NIC), Wi-Fi card, or cellularmodem.

Any of the software can be physically located at various positions,including being distributed such that portions of the functions areimplemented at different physical locations.

Additionally, systems of the invention can be provided to includereference data. Any suitable genomic data may be stored for use withinthe system. Examples include, but are not limited to: comprehensive,multi-dimensional maps of the key genomic changes in major types andsubtypes of cancer from The Cancer Genome Atlas (TCGA); a catalog ofgenomic abnormalities from The International Cancer Genome Consortium(ICGC); a catalog of somatic mutations in cancer from COSMIC; the latestbuilds of the human genome and other popular model organisms; up-to-datereference SNPs from dbSNP; gold standard indels from the 1000 GenomesProject and the Broad Institute; exome capture kit annotations fromIllumina, Agilent, Nimblegen, and Ion Torrent; transcript annotations;small test data for experimenting with pipelines (e.g., for new users).

In some embodiments, data is made available within the context of adatabase included in a system. Any suitable database structure may beused including relational databases, object-oriented databases, andothers. In some embodiments, reference data is stored in a relationaldatabase such as a “not-only SQL” (NoSQL) database. In certainembodiments, a graph database is included within systems of theinvention. It is also to be understood that the term “database” as usedherein is not limited to one single database; rather, multiple databasescan be included in a system. For example, a database can include two,three, four, five, six, seven, eight, nine, ten, fifteen, twenty, ormore individual databases, including any integer of databases therein,in accordance with embodiments of the invention. For example, onedatabase can contain public reference data, a second database cancontain test data from a patient, a third database can contain data fromhealthy subjects, and a fourth database can contain data from sicksubjects with a known condition or disorder. It is to be understood thatany other configuration of databases with respect to the data containedtherein is also contemplated by the methods described herein.

EXEMPLARY EMBODIMENTS (A)

The present description provides the following embodiments, with someembodiments of this paragraph referring to other numbered embodiments ofthis paragraph:

1. A method for detecting a disease state in a subject, the methodcomprising:

isolating a biological test sample from the subject, wherein thebiological test sample comprises a plurality of cell-free ribonucleicacid (cfRNA) molecules;

extracting the plurality of cfRNA molecules from the biological testsample;

performing a sequencing procedure on the extracted cfRNA molecules togenerate a plurality of sequence reads;

performing a filtering procedure to generate an excluded population ofsequence reads that originate from one or more healthy cells, and anon-excluded population of sequence reads;

performing a quantification procedure on one or more of the non-excludedsequence reads; and

detecting the disease state in the subject when one or more of thenon-excluded sequence reads exceeds a threshold.

2. The method of embodiment 1, further comprising quantifying the cfRNAmolecules that are extracted from the biological test sample.3. The method of embodiment 1, wherein the sequencing procedurecomprises:

performing a reverse transcription procedure on the cfRNA molecules toproduce a plurality of cDNA/RNA hybrid molecules;

degrading the RNA of the hybrid molecules to produce a plurality ofsingle-stranded cDNA molecule templates;

synthesizing a plurality of double-stranded DNA molecules from thesingle-stranded cDNA molecule templates;

ligating a plurality of double-stranded DNA adapters to the plurality ofdouble-stranded DNA molecules, thereby producing a sequencing library;and

performing a sequencing procedure on at least a portion of thesequencing library to obtain a plurality of sequence reads.

4. The method of embodiment 3, wherein synthesizing the double-strandedDNA molecules comprises performing a strand-displacement reversetranscriptase procedure.5. The method of embodiment 3, wherein the sequencing procedurecomprises a whole transcriptome sequencing procedure.6. The method of embodiment 3, wherein the sequencing procedurecomprises a targeted sequencing procedure, and wherein one or more ofthe cfRNA molecules are enriched from the biological test sample beforepreparing the sequencing library.7. The method of embodiment 6, wherein one or more cfRNA moleculesindicative of the disease state are targeted for enrichment.8. The method according to embodiment 7, wherein the targeted cfRNAmolecules are derived from a gene selected from the group consisting ofAGR2, BPIFA1, CASP14, CSN1S1, DISP2, EIF2D, FABP7, GABRG1, GNAT3, GRHL2,HOXC10, IDI2-AS1, KRT16P2, LALBA, LINC00163, NKX2-1, OPN1SW, PADI3,PTPRZ1, ROS1, S100A7, SCGB2A2, SERPINB5, SFTA3, SFTPA2, SLC34A2, TFF1,VTCN1, WFDC2, MUC5B, SMIM22, CXCL17, RNU1-1, and KLK5.9. The method according to embodiment 7, wherein the targeted cfRNAmolecules are derived from a gene selected from the group consisting ofROS1, NKX2, GGTLC1, SLC34A2, SFTPA2, BPIFA1, SFTA3, GABRG1, AGR2, GNAT3,MUC5B, SMIM22, CXCL17, and WFDC2.10. The method according to embodiment 7, wherein the targeted cfRNAmolecules are derived from a gene selected from the group consisting ofSCGB2A2, CSN1S1, VTCN-1, FABP7, LALBA, RNU1-1, OPN1SW, CASP14, KLK5, andWFDC2.11. The method according to embodiment 7, wherein the targeted cfRNAmolecules are derived from the AKR1B10, C3, and/or PIEZO2 genes.12. The method of embodiment 1, wherein the plurality of cfRNA moleculescomprises one or more dark channel cfRNA molecules.13. The method of embodiment 1, wherein the filtering procedurecomprises:

comparing each sequence read from the cfRNA molecules extracted from thebiological test sample to a control data set of RNA sequences;

identifying one or more sequence reads that match one or more sequencereads in the control data set of RNA sequences; and

placing each sequence read that matches one or more sequence reads inthe control data set of RNA sequences in the excluded population ofsequence reads.

14. The method of embodiment 13, wherein the control data set of RNAsequences comprises a plurality of sequence reads obtained from one ormore healthy subjects.15. The method of embodiment 13, wherein the control data set of RNAsequences comprises a plurality of sequence reads obtained from aplurality of blood cells from the subject.16. The method of embodiment 15, wherein the blood cells comprise whiteblood cells (WBCs).17. The method of any one of embodiments 1-16, wherein the disease stateis a cardiovascular disease state.18. The method of any one of embodiments 1-16, wherein the disease stateis a liver disease state.19. The method of any one of embodiments 1-16, wherein the disease stateis a cancerous disease state.20. The method of embodiment 19, wherein the cancerous disease statecomprises a carcinoma, a sarcoma, a myeloma, a leukemia, a lymphoma, ablastoma, a germ cell tumor, or any combination thereof.21. The method of embodiment 20, wherein the carcinoma is anadenocarcinoma.22. The method of embodiment 20, wherein the carcinoma is a squamouscell carcinoma.23. The method of embodiment 20, wherein the carcinoma is selected fromthe group consisting of: small cell lung cancer, non-small-cell lung,nasopharyngeal, colorectal, anal, liver, urinary bladder, testicular,cervical, ovarian, gastric, esophageal, head-and-neck, pancreatic,prostate, renal, thyroid, melanoma, and breast carcinoma.24. The method of embodiment 23, wherein the breast carcinoma is hormonereceptor negative breast carcinoma or triple negative breast carcinoma.25. The method of embodiment 20, wherein the sarcoma is selected fromthe group consisting of: osteosarcoma, chondrosarcoma, leiomyosarcoma,rhabdomyosarcoma, mesothelial sarcoma (mesothelioma), fibrosarcoma,angiosarcoma, liposarcoma, glioma, and astrocytoma.26. The method of embodiment 20, wherein the leukemia is selected fromthe group consisting of: myelogenous, granulocytic, lymphatic,lymphocytic, and lymphoblastic leukemia.27. The method of embodiment 20, wherein the lymphoma is selected fromthe group consisting of: Hodgkin's lymphoma and Non-Hodgkin's lymphoma.28. The method of embodiment 1, further comprising determining a tissueof origin of the disease, wherein the tissue of origin is selected fromthe group consisting of pancreatic tissue, liver issue, lung tissue,brain tissue, uterus tissue, renal tissue, breast tissue, fat, colontissue, rectum tissue, heart tissue, skeletal muscle tissue, prostatetissue and thyroid tissue.29. The method of embodiment 1, wherein the biological test samplecomprises a biological fluid.30. The method of embodiment 29, wherein the biological fluid comprises:blood, plasma, serum, urine, saliva, pleural fluid, pericardial fluid,cerebrospinal fluid (CSF), peritoneal fluid, or any combination thereof.31. The method of embodiment 1, wherein the disease state is determinedby inputting the sequence reads into a machine learning or deep learningmodel.32. The method of embodiment 31, wherein the machine learning or deeplearning model comprises logistic regression, random forest, gradientboosting machine, Naïve Bayes, neural network, or multinomialregression.33. The method of embodiment 31, wherein the method further comprises:

performing or having performed a computational analysis on thenon-excluded sequence reads to generate values of one or more features,wherein the features comprise a quantity of the one or more non-excludedsequence reads;

inputting the values of the one or more features into the machinelearning or deep learning model to generate a disease state predictionfor the subject, and wherein the machine learning or deep learning modeltransforms the values of the one or more features to the disease stateprediction for the subject through a function comprising learnedweights; and

providing the disease state prediction for the subject.

34. A computer-implemented method for identifying one or more RNAsequences indicative of a disease state, the method comprising:

obtaining, by a computer system, a first set of sequence reads from aplurality of RNA molecules from a first test sample from a subject knownto have the disease, wherein the first test sample comprises a pluralityof cell-free RNA (cfRNA) molecules;

obtaining, by a computer system, a second set of sequence reads from aplurality of RNA molecules from a control sample;

detecting, by a computer system, one or more RNA sequences that arepresent in the first set of sequence reads, and that are not present inthe second set of sequence reads, to identify one or more RNA sequencesthat are indicative of the disease state.

35. The method of embodiment 34, wherein the control sample comprises aplurality of RNA molecules from one or more healthy subjects.36. The method of embodiment 35, wherein the second set of sequencereads comprises RNA sequence information obtained from a publicdatabase.37. The method of embodiment 36, wherein the control sample comprises aplurality of RNA molecules from a plurality of blood cells from thesubject.38. The method of any one of embodiments 34-37, wherein the first testsample comprises: blood, plasma, serum, urine, saliva, pleural fluid,pericardial fluid, cerebrospinal fluid (CSF), peritoneal fluid, or anycombination thereof 39. The method of embodiment 38, wherein the firsttest sample comprises plasma.40. The method of any one of embodiment 37, wherein the blood cellscomprise white blood cells.41. The method of any one of embodiments 34-37, wherein the diseasestate is a cardiovascular disease state.42. The method of any one of embodiments 34-37, wherein the diseasestate is a liver disease state.43. The method of any one of embodiments 34-37, wherein the diseasestate is a cancerous disease state.44. The method of embodiment 36, wherein the cancerous disease statecomprises a carcinoma, a sarcoma, a myeloma, a leukemia, a lymphoma, ablastoma, a germ cell tumor, or any combination thereof.45. The method of embodiment 44, wherein the carcinoma is anadenocarcinoma.46. The method of embodiment 44, wherein the carcinoma is a squamouscell carcinoma.47. The method of embodiment 44, wherein the carcinoma is selected fromthe group consisting of: small cell lung cancer, non-small-cell lung,nasopharyngeal, colorectal, anal, liver, urinary bladder, testicular,cervical, ovarian, gastric, esophageal, head-and-neck, pancreatic,prostate, renal, thyroid, melanoma, and breast carcinoma.48. The method of embodiment 47, wherein the breast carcinoma is hormonereceptor negative breast carcinoma or triple negative breast carcinoma.49. The method of embodiment 44, wherein the sarcoma is selected fromthe group consisting of: osteosarcoma, chondrosarcoma, leiomyosarcoma,rhabdomyosarcoma, mesothelial sarcoma (mesothelioma), fibrosarcoma,angiosarcoma, liposarcoma, glioma, and astrocytoma.50. The method of embodiment 44, wherein the leukemia is selected fromthe group consisting of: myelogenous, granulocytic, lymphatic,lymphocytic, and lymphoblastic leukemia.51. The method of embodiment 44, wherein the lymphoma is selected fromthe group consisting of: Hodgkin's lymphoma and Non-Hodgkin's lymphoma.52. The method of embodiment 34, wherein the disease state is determinedby inputting the one or more RNA sequences into a machine learning ordeep learning model.53. The method of embodiment 52, wherein the machine learning or deeplearning model comprises logistic regression, random forest, gradientboosting machine, Naïve Bayes, neural network, or multinomialregression.54. The method of embodiment 52, wherein the method further comprises:

performing or having performed a computational analysis on the one ormore RNA sequences to generate values of one or more features, whereinthe features comprise a quantity of the one or more RNA sequences;

inputting the values of the one or more features into the machinelearning or deep learning model to generate a disease state predictionfor the subject, and wherein the machine learning or deep learning modeltransforms the values of the one or more features to the disease stateprediction for the subject through a function comprising learnedweights; and providing the disease state prediction for the subject.

55. A computer-implemented method for detecting one or moretumor-derived RNA molecules in a subject, the method comprising:

obtaining, by a computer system, a first set of sequence reads from aplurality of RNA molecules from a first test sample from a subject knownto have a tumor, wherein the first test sample comprises a plurality ofcell-free RNA (cfRNA) molecules;

obtaining, by a computer system, a second set of sequence reads from aplurality of RNA molecules from a plurality of blood cells from thesubject; and

detecting, by a computer system, one or more RNA sequences that arepresent in the first set of sequence reads, and that are not present inthe second set of sequence reads, to detect the one or moretumor-derived RNA molecules in the subject.

56. The method of embodiment 55, wherein the plurality of blood cellscomprise white blood cells.57. The method of embodiment 55, wherein the first test samplecomprises: blood, plasma, serum, urine, saliva, pleural fluid,pericardial fluid, cerebrospinal fluid (CSF), peritoneal fluid, or anycombination thereof.58. The method of embodiment 55, wherein the first test sample comprisesplasma.59. The method of embodiment 55, wherein the tumor comprises acarcinoma, a sarcoma, a myeloma, a leukemia, a lymphoma, a blastoma, agerm cell tumor, or any combination thereof.60. The method of embodiment 59, wherein the carcinoma is anadenocarcinoma.61. The method of embodiment 59, wherein the carcinoma is a squamouscell carcinoma.62. The method of embodiment 59, wherein the carcinoma is selected fromthe group consisting of: small cell lung cancer, non-small-cell lung,nasopharyngeal, colorectal, anal, liver, urinary bladder, testicular,cervical, ovarian, gastric, esophageal, head-and-neck, pancreatic,prostate, renal, thyroid, melanoma, and breast carcinoma.63. The method of embodiment 62, wherein the breast carcinoma is hormonereceptor negative breast carcinoma or triple negative breast carcinoma.64. The method of embodiment 59, wherein the sarcoma is selected fromthe group consisting of: osteosarcoma, chondrosarcoma, leiomyosarcoma,rhabdomyosarcoma, mesothelial sarcoma (mesothelioma), fibrosarcoma,angiosarcoma, liposarcoma, glioma, and astrocytoma.65. The method of embodiment 59, wherein the leukemia is selected fromthe group consisting of: myelogenous, granulocytic, lymphatic,lymphocytic, and lymphoblastic leukemia.66. The method of embodiment 59, wherein the lymphoma is selected fromthe group consisting of: Hodgkin's lymphoma and Non-Hodgkin's lymphoma.67. The method of embodiment 55, wherein the method further comprisesdetermining cancer status of the subject, wherein determining the cancerstatus is determined by inputting the one or more tumor-derived RNAmolecules into a machine learning or deep learning model.68. The method of embodiment 67, wherein the machine learning or deeplearning model comprises logistic regression, random forest, gradientboosting machine, Naïve Bayes, neural network, or multinomialregression.69. The method of embodiment 67, wherein the method further comprises:

performing or having performed a computational analysis on the one ormore tumor-derived RNA molecules to generate values of one or morefeatures, wherein the features comprise a quantity of the one or moretumor-derived RNA molecules;

inputting the values of the one or more features into the machinelearning or deep learning model to generate a disease state predictionfor the subject, and wherein the machine learning or deep learning modeltransforms the values of the one or more features to the disease stateprediction for the subject through a function comprising learnedweights; and providing the cancer status for the subject.

70. A method for detecting a presence of a cancer, determining a cancerstage, monitoring a cancer progression, and/or determining a cancer typein a subject known to have or suspected of having a cancer, the methodcomprising:

(a) obtaining a biological test sample from the subject, wherein thebiological test sample comprises a plurality of cell-free ribonucleicacid (cfRNA) molecules;

(b) quantitatively detecting the presence of one or more nucleic acidsequences derived from one or more target RNA molecules in thebiological test sample to determine a tumor RNA score, wherein the oneor more target RNA molecules are selected from the target RNA moleculeslisted on any one of Tables 1-3; and

(c) detecting the presence of the cancer, determining the cancer stage,monitoring the cancer progression, and/or determining the cancer type inthe subject when the tumor RNA score exceeds a threshold value.

71. The method of embodiment 70, wherein quantitatively detecting thepresence of the sequences derived from the RNA markers comprisesconducting a sequencing procedure.72. The method of embodiment 71, wherein the sequencing procedurecomprises whole transcriptome sequencing of the cfRNA molecules in thebiological test sample.73. The method of embodiment 70, wherein quantitatively detecting thepresence of the sequences derived from the target RNA moleculescomprises sequencing, NGS sequencing, Sanger sequencing, microarrayanalysis, reverse transcription PCR, real-time PCR, quantitativereal-time PCR, digital PCR, digital droplet PCR, digital emulsion PCR,multiplex PCR, hybrid capture, oligonucleotide ligation assays, or anycombination thereof.74. The method of embodiment 70, wherein quantitatively detecting thepresence of the sequences derived from the target RNA moleculescomprises microarray analysis.75. The method of embodiment 71, further comprising enriching the cfRNAmolecules in the biological test sample for one or more RNA markersprior to quantitatively detecting the presence of a sequence readderived from one or more target RNA molecules.76. The method of embodiment 70, wherein the cancer comprises acarcinoma, a sarcoma, a myeloma, a leukemia, a lymphoma, a blastoma, agerm cell tumor, or any combination thereof.77. The method of embodiment 76, wherein the carcinoma is anadenocarcinoma.78. The method of embodiment 76, wherein the carcinoma is a squamouscell carcinoma.79. The method of embodiment 76, wherein the carcinoma is selected fromthe group consisting of: small cell lung cancer, non-small-cell lung,nasopharyngeal, colorectal, anal, liver, urinary bladder, testicular,cervical, ovarian, gastric, esophageal, head-and-neck, pancreatic,prostate, renal, thyroid, melanoma, and breast carcinoma.80. The method of embodiment 79, wherein the breast carcinoma is hormonereceptor negative breast carcinoma or triple negative breast carcinoma.81. The method of embodiment 79, wherein the sarcoma is selected fromthe group consisting of: osteosarcoma, chondrosarcoma, leiomyosarcoma,rhabdomyosarcoma, mesothelial sarcoma (mesothelioma), fibrosarcoma,angiosarcoma, liposarcoma, glioma, and astrocytoma.82. The method of embodiment 76, wherein the leukemia is selected fromthe group consisting of: myelogenous, granulocytic, lymphatic,lymphocytic, and lymphoblastic leukemia.83. The method of embodiment 76, wherein the lymphoma is selected fromthe group consisting of: Hodgkin's lymphoma and Non-Hodgkin's lymphoma.84. The method of embodiment 70 wherein the cancer stage is selectedfrom stage I cancer, stage II cancer, stage III cancer, and stage IVcancer.85. The method of embodiment 70, wherein the tumor RNA score isdetermined by inputting the sequence reads into a machine learning ordeep learning model.86. The method of embodiment 85, wherein the machine learning or deeplearning model comprises logistic regression, random forest, gradientboosting machine, Naïve Bayes, neural network, or multinomialregression.87. The method of embodiment 85, wherein the method further comprises:

performing or having performed a computational analysis on the one ormore target RNA molecules to generate values of one or more features,wherein the features comprise a quantity of the one or more target RNAmolecules;

inputting the values of the one or more features into the machinelearning or deep learning model to generate a cancer prediction for thesubject, the cancer prediction comprising detecting a presence of acancer, determining a cancer stage, monitoring a cancer progression,and/or determining a cancer type, and wherein the machine learning ordeep learning model transforms the values of the one or more features tothe cancer prediction for the subject through a function comprisinglearned weights; and

providing the cancer prediction for the subject.

88. The method of embodiment 70, wherein the one or more target RNAmolecules comprise one or more of the markers listed on Table 1.89. The method of embodiment 70, wherein the one or more target RNAmolecules comprise one or more of the markers listed on Table 2.90. The method of embodiment 70, wherein the one or more target RNAmolecules include one or more of the markers listed on Table 3.91. A computer-implemented method for detecting the presence of a cancerin a subject, the method comprising:

receiving a data set in a computer comprising a processor and acomputer-readable medium, wherein the data set comprises a plurality ofsequence reads obtained from a plurality of ribonucleic acid (RNA)molecules in a biological test sample from the subject, and wherein thecomputer-readable medium comprises instructions that, when executed bythe processor, cause the computer to:

determine an expression level of a plurality of target RNA molecules inthe biological test sample;

compare the expression level of each of the target RNA molecules to anRNA tissue score matrix to determine a cancer indicator score for eachtarget RNA molecule;

aggregate the cancer indicator score for each target RNA molecule togenerate a cancer indicator score for the biological test sample; and

detect the presence of the cancer in the subject when the cancerindicator score for the biological test sample exceeds a thresholdvalue.

92. The method according to embodiment 91, wherein the target RNAmolecules have an expression level in subjects with a known cancerstatus that exceeds their expression level in healthy subjects.93. The method according to embodiment 91, wherein the expression levelof a target RNA molecule in a subject with a known cancer status is atleast 10 times greater than the expression level of the target RNAmolecule in a healthy subject.94. The method according to embodiment 91, wherein the target RNAmolecules are not detectable in a biological test sample from a healthysubject.95. The method according to embodiment 91, wherein the number of targetRNA molecules ranges from 1 to 20.96. The method according to embodiment 91, wherein the threshold valueranges from 0.5 to 5 reads per million (RPM).97. The method according to embodiment 91, further comprisingdetermining a cancer cell type or tissue of origin of the cancer in thesubject based on the expression level of one or more of the target RNAmolecules, the cancer indicator score for one or more of the target RNAmolecules, the cancer indicator score for the biological test sample, orany combination thereof.98. The method according to embodiment 91, further comprisingtherapeutically classifying the subject into one or more of a pluralityof treatment categories based on the expression level of one or more ofthe target RNA molecules, the cancer indicator score for one or more ofthe target RNA molecules, the cancer indicator score for the biologicaltest sample, or any combination thereof.99. The method according to any one of the preceding embodiments,wherein the computer is configured to generate a report that comprises:an expression level of one or more of the target RNA molecules, a cancerindicator score for one or more of the target RNA molecules, a cancerindicator score for the biological test sample, an indication of thepresence or absence of the cancer in the subject, an indication of thecancer cell type of tissue of origin of the cancer in the subject, atherapeutic classification for the subject, or any combination thereof.100. The method according to any one of the preceding embodiments,wherein the plurality of RNA molecules comprise cell-free RNA (cfRNA)molecules.101. The method according to any one of the preceding embodiments,wherein the plurality of RNA molecules comprise circulating tumor RNA(ctRNA) molecules.102. The method of any one of the preceding embodiments, wherein theplurality of RNA molecules in the biological test sample comprise RNAfrom cancerous and non-cancerous cells.103. The method according to any one of the preceding embodiments,wherein the biological test sample comprises a biological fluid.104. The method according to embodiment 103, wherein the biologicalfluid comprises blood, plasma, serum, urine, saliva, pleural fluid,pericardial fluid, cerebrospinal fluid (CSF), peritoneal fluid, or anycombination thereof.105. The method according to any one of embodiments 91-103, wherein thebiological test sample comprises a tissue biopsy.106. The method according to embodiment 105, wherein the tissue biopsyis a cancerous tissue biopsy.107. The method according to embodiment 105, wherein the tissue biopsyis a healthy tissue biopsy.108. The method according to any one of the preceding embodiments,wherein the cancer comprises a carcinoma, a sarcoma, a myeloma, aleukemia, a lymphoma, a blastoma, a germ cell tumor, or any combinationthereof109. The method according to embodiment 108, wherein the carcinoma is anadenocarcinoma.110. The method according to embodiment 108, wherein the carcinoma is asquamous cell carcinoma.111. The method according to embodiment 108, wherein the carcinoma isselected from the group consisting of: small cell lung cancer,non-small-cell lung, nasopharyngeal, colorectal, anal, liver, urinarybladder, testicular, cervical, ovarian, gastric, esophageal,head-and-neck, pancreatic, prostate, renal, thyroid, melanoma, andbreast carcinoma.112. The method according to embodiment 108, wherein the breast canceris hormone receptor negative breast cancer or triple negative breastcancer.113. The method according to embodiment 108, wherein the sarcoma isselected from the group consisting of: osteosarcoma, chondrosarcoma,leiomyosarcoma, rhabdomyosarcoma, mesothelial sarcoma (mesothelioma),fibrosarcoma, angiosarcoma, liposarcoma, glioma, and astrocytoma.114. The method according to embodiment 108, wherein the leukemia isselected from the group consisting of: myelogenous, granulocytic,lymphatic, lymphocytic, and lymphoblastic leukemia.115. The method according to embodiment 108, wherein the lymphoma isselected from the group consisting of: Hodgkin's lymphoma andNon-Hodgkin's lymphoma.116. A method for constructing an RNA tissue score matrix, the methodcomprising:

compiling a plurality of RNA sequence reads obtained from a plurality ofsubjects to generate an RNA expression matrix; and

normalizing the RNA expression matrix with a tissue-specific RNAexpression matrix to construct the RNA tissue score matrix.

117. The method according to embodiment 116, wherein the tissue-specificRNA expression matrix comprises a plurality of reference human tissues.118. The method according to embodiment 116, wherein the RNA sequencereads are obtained from a plurality of healthy subjects to construct ahealthy RNA tissue score matrix.119. The method according to embodiment 116, wherein the RNA sequencereads are obtained from a plurality of subjects having a known cancertype to construct a cancer RNA tissue score matrix.120. The method according to embodiment 116, wherein the RNA sequencereads are obtained from a plurality of subjects having a known liverdisease to construct a liver disease RNA tissue score matrix.121. A method for detecting a presence of a liver disease, determining astage of a liver disease, and/or monitoring progression of a liverdisease, the method comprising:

(a) obtaining a biological test sample from the subject, wherein thebiological test sample comprises a plurality of cell-free ribonucleicacid (cfRNA) molecules;

(b) quantitatively detecting the presence of a nucleic acid sequencederived from one or more target RNA molecules in the test sample todetermine an RNA score from the one or more target RNA molecules,wherein the one or more target RNA molecules are derived from theAKR1B10 gene; and

(c) detecting a presence of a liver disease, determining a stage of aliver disease, and/or monitoring progression of a liver disease in thesubject when the tumor RNA score exceeds a threshold value.

122. The method of embodiment 121, wherein quantitatively detecting thepresence of the sequences derived from the target RNA moleculescomprises conducting a sequencing procedure.123. The method of embodiment 122, wherein the sequencing procedurecomprises whole transcriptome sequencing of the cfRNA molecules in thebiological test sample.124. The method of embodiment 121, wherein quantitatively detecting thepresence of the sequences derived from the target RNA moleculescomprises sequencing, NGS sequencing, Sanger sequencing, microarrayanalysis, reverse transcription PCR, real-time PCR, quantitativereal-time PCR, digital PCR, digital emulsion PCR, droplet digital PCR,multiplex PCR, hybrid capture, oligonucleotide ligation assays, or anycombination thereof.125. The method of embodiment 121, wherein quantitatively detecting thepresence of the sequences derived from the target RNA moleculescomprises microarray analysis.126. The method of embodiment 121, further comprising enriching thecfRNA molecules in the biological test sample for one or more RNAmarkers prior to quantitatively detecting the presence of a sequenceread derived from one or more target RNA molecules.127. The method of embodiment 121, wherein the liver disease is selectedfrom non-alcoholic fatty liver disease (NAFLD), alcoholicsteatohepatitis (ASH), non-alcoholic steatohepatitis (NASH), liverfibrosis, liver cirrhosis, hepatocellular carcinoma (HCC), and anycombination thereof.128. The method of embodiment 121, wherein the tumor RNA score isdetermined by inputting the sequence reads into a machine learned model.129. The method of embodiment 121, wherein the machine learned modelcomprises a logistic regression classifier. logistic regressionpredictor, a random forest predictor, a gradient boosting machine, NaïveBayes classifier, support vector machine, or a neural network.130. The method of embodiment 121, wherein the one or more RNA markerscomprise one or more of the markers listed on Table 1.131. A computer-implemented method for detecting the presence of a liverdisease in a subject, the method comprising:

receiving a data set in a computer comprising a processor and acomputer-readable medium, wherein the data set comprises a plurality ofsequence reads obtained from a plurality of ribonucleic acid (RNA)molecules in a biological test sample from the subject, and wherein thecomputer-readable medium comprises instructions that, when executed bythe processor, cause the computer to:

determine an expression level of a plurality of target RNA molecules inthe biological test sample;

compare the expression level of each of the target RNA molecules to anRNA tissue score matrix to determine a liver disease indicator score foreach target RNA molecule;

aggregate the liver disease indicator score for each target RNA moleculeto generate a liver indicator score for the biological test sample; and

detect the presence of the liver disease in the subject when the liverdisease indicator score for the biological test sample exceeds athreshold value.

132. The method according to embodiment 131, wherein the target RNAmolecules have an expression level in subjects with a known liverdisease status that exceeds their expression level in healthy subjects.133. The method according to embodiment 132, wherein the expressionlevel of a target RNA molecule in a subject with a known liver diseasestatus is at least 10 times greater than the expression level of thetarget RNA molecule in a healthy subject.134. The method according to embodiment 132, wherein the target RNAmolecules are not detectable in a biological test sample from a healthysubject.135. The method according to embodiment 131, wherein the number oftarget RNA molecules ranges from 1 to 20.136. The method according to embodiment 131, wherein the threshold valueranges from 0.5 to 5 reads per million (RPM).137. The method according to any one of embodiments 121-136, wherein thecomputer is configured to generate a report that comprises: anexpression level of one or more of the target RNA molecules, a liverdisease indicator score for one or more of the target RNA molecules, aliver indicator score for the biological test sample, an indication ofthe presence or absence of the liver disease in the subject, atherapeutic classification for the subject, or any combination thereof.138. The method according to 121-137, wherein the plurality of RNAmolecules comprise cell-free RNA (cfRNA) molecules.139. The method according to 121-137, wherein the plurality of RNAmolecules comprise circulating tumor RNA (ctRNA) molecules.140. The method of according to any one of embodiments 121-137, whereinthe plurality of RNA molecules in the biological test sample compriseRNA from cells from one or more subjects known to have a liver diseaseand cells from a healthy subject.141. The method according to any one of the preceding embodiments121-137, wherein the biological test sample comprises a biologicalfluid.142. The method according to embodiment 144, wherein the biologicalfluid comprises blood, plasma, serum, urine, saliva, pleural fluid,pericardial fluid, cerebrospinal fluid (CSF), peritoneal fluid, or anycombination thereof.143. The method according to any one of embodiments 121-141, wherein thebiological test sample comprises a tissue biopsy.144. The method according to embodiment 144, wherein the tissue biopsyis a liver tissue biopsy.145. The method according to embodiment 144, wherein the tissue biopsyis a healthy tissue biopsy.146. The method according to any one of embodiments 121-145, wherein theliver disease condition is selected from non-alcoholic fatty liverdisease (NAFLD), alcoholic steatohepatitis (ASH), non-alcoholicsteatohepatitis (NASH), liver fibrosis, liver cirrhosis, hepatocellularcarcinoma (HCC), and any combination thereof.

EXEMPLARY EMBODIMENTS (B)

The present description provides the following embodiments, with someembodiments of this paragraph referring to other numbered embodiments ofthis paragraph:

1. A method of measuring a subpopulation of cell-free RNA (cfRNA)molecules of a subject, the method comprising:

a. sequencing the cfRNA molecules to produce cfRNA sequence reads;

b. sequencing cellular RNA extracted from cells of the subject toproduce cellular sequence reads;

c. performing a filtering procedure to produce a non-excluded populationof cfRNA sequence reads, wherein the filtering comprises excluding cfRNAsequence reads that match one or more of the cellular sequence reads;and

d. quantifying one or more of the non-excluded sequence reads.

2. The method of embodiment 1, wherein sequencing the cfRNA moleculescomprises reverse transcription to produce cDNA molecules, andsequencing the cDNA molecules to produce the cfRNA sequence reads.3. The method of embodiment 1, wherein sequencing the cfRNA moleculescomprises:

a. reverse transcribing the cfRNA molecules to produce a plurality ofcDNA/RNA hybrid molecules;

b. synthesizing a plurality of double-stranded cDNA molecules from thecDNA/RNA hybrid molecules;

c. ligating a plurality of double-stranded polynucleotide adapters tothe plurality of double-stranded cDNA molecules, thereby producing asequencing library; and

d. sequencing at least a portion of the sequencing library to producethe cfRNA sequence reads.

4. The method of any one of embodiments 1-3, wherein sequencing thecfRNA molecules comprises whole transcriptome sequencing.5. The method of any one of embodiments 1-3, wherein sequencing thecfRNA molecules comprises enriching the cfRNA molecules or cDNAmolecules thereof for one or more target polynucleotides.6. The method of any one of embodiments 1-5, wherein the non-excludedsequence reads only include reads or read pairs that overlap anexon-exon junction.7. The method of any one of embodiments 1-6, wherein the cfRNA is from abiological test sample of the subject comprising a biological fluid.8. The method of embodiment 7, wherein the biological fluid comprisesblood, plasma, serum, urine, saliva, pleural fluid, pericardial fluid,cerebrospinal fluid (CSF), peritoneal fluid, or any combination thereof.9. The method of any one of embodiments 1-8, wherein the cfRNA moleculesare obtained from blood, a blood fraction, plasma, or serum of thesubject.10. The method of any one of embodiments 1-9, wherein the cells areobtained from blood or a blood fraction of the subject.11. The method of embodiment 10, wherein the cfRNA molecules and thecells are obtained from a blood sample of the subject.12. The method of any one of embodiments 1-11, wherein the cells arewhite blood cells (WBCs).13. The method of any one of embodiments 1-12, further comprisingdetecting a condition of the subject, wherein detecting the conditioncomprises detecting one or more non-excluded sequence reads above athreshold.14. The method of embodiment 13, wherein detecting one or morenon-excluded sequence reads above a threshold comprises (i) detection,(ii) detection above background, or (iii) detection at a level that isgreater than a level of corresponding sequence reads in subjects that donot have the condition.15. The method of embodiment 13, wherein detecting one or morenon-excluded sequence reads above a threshold comprises detecting theone or more sequence reads at a level that is at least about 10 timesgreater than a level of corresponding sequence reads in subjects that donot have the condition.16. The method of embodiment 13, wherein detecting one or morenon-excluded sequence reads above a threshold comprises detection abovea threshold value of 0.5 to 5 reads per million (RPM).17. The method of embodiment 13, wherein detecting one or morenon-excluded sequence reads above a threshold comprises:

(a) determining an expression level of a plurality of target cfRNAmolecules;

(b) determining an indicator score for each target cfRNA molecule bycomparing the expression level of each of the target cfRNA molecules toan RNA tissue score matrix;

(c) aggregating the indicator scores for each target cfRNA molecule;and,

(d) detecting presence of the condition in the subject when theindicator score exceeds a threshold value.

18. The method of embodiment 13, wherein detecting one or morenon-excluded sequence reads above a threshold comprises inputting thesequence reads into a machine learning or deep learning model.19. The method of embodiment 18, wherein the machine learning or deeplearning model comprises logistic regression, random forest, gradientboosting machine, Naïve Bayes, neural network, or multinomialregression.20. The method of embodiment 18, wherein the machine learning or deeplearning model transforms the values of the one or more features to thedisease state prediction for the subject through a function comprisinglearned weights.21. The method of any one of embodiments 13-20, wherein the condition iscardiovascular disease, liver disease, or a cancer.22. The method of embodiment 21, wherein the condition is a liverdisease selected from non-alcoholic fatty liver disease (NAFLD),alcoholic steatohepatitis (ASH), non-alcoholic steatohepatitis (NASH),liver fibrosis, liver cirrhosis, hepatocellular carcinoma (HCC), and anycombination thereof.23. The method of embodiment 21, wherein the condition is a cancercomprising:

(i) a carcinoma, a sarcoma, a myeloma, a leukemia, a lymphoma, ablastoma, a germ cell tumor, or any combination thereof;

(ii) a carcinoma selected from the group consisting of adenocarcinoma,squamous cell carcinoma, small cell lung cancer, non-small-cell lungcancer, nasopharyngeal, colorectal, anal, liver, urinary bladder,testicular, cervical, ovarian, gastric, esophageal, head-and-neck,pancreatic, prostate, renal, thyroid, melanoma, and breast carcinoma;

(iii) hormone receptor negative breast carcinoma or triple negativebreast carcinoma;

(iv) a sarcoma selected from the group consisting of: osteosarcoma,chondrosarcoma, leiomyosarcoma, rhabdomyosarcoma, mesothelial sarcoma(mesothelioma), fibrosarcoma, angiosarcoma, liposarcoma, glioma, andastrocytoma;

(v) a leukemia selected from the group consisting of myelogenous,granulocytic, lymphatic, lymphocytic, and lymphoblastic leukemia; or

(vi) a lymphoma selected from the group consisting of: Hodgkin'slymphoma and Non-Hodgkin's lymphoma.

24. The method of any one of embodiments 13-23, wherein the one or morenon-excluded sequence reads detected above a threshold originate from atranscript of a diseased cell, and the method further comprises the stepof identifying the tissue origin of the diseased cell.25. The method of embodiment 24, wherein the tissue origin is selectedfrom the group consisting of pancreatic tissue, liver tissue, lungtissue, brain tissue, uterus tissue, renal tissue, breast tissue, fat,colon tissue, rectum tissue, heart tissue, skeletal muscle tissue,prostate tissue and thyroid tissue.26. The method of any one of embodiments 13-23, wherein the one or morenon-excluded sequence reads detected above a threshold originate from atranscript of a cancer cell, and the method further comprisesdetermining a cancer cell type or tissue of origin of the cancer in thesubject.27. The method of any one of embodiments 13-26, wherein the one or morenon-excluded sequence reads detected above a threshold are sequencereads of one or more target polynucleotides enriched from the cfRNAmolecules or amplicons thereof.28. The method of any one of embodiments 13-27, wherein the one or morenon-excluded sequence reads detected above a threshold are sequencereads for cfRNA molecules derived from 1 to 20 target genes.29. The method of any one of embodiments 13-28, wherein (i) thecondition is cancer, and (ii) the one or more non-excluded sequencereads detected above a threshold are sequence reads for cfRNA moleculesderived from one or more genes selected from the group consisting of:AGR2, BPIFA1, CASP14, CSN1S1, DISP2, EIF2D, FABP7, GABRG1, GNAT3, GRHL2,HOXC10, IDI2-AS1, KRT16P2, LALBA, LINC00163, NKX2-1, OPN1SW, PADI3,PTPRZ1, ROS1, S100A7, SCGB2A2, SERPINB5, SFTA3, SFTPA2, SLC34A2, TFF1,VTCN1, WFDC2, MUC5B, SMIM22, CXCL17, RNU1-1, and KLK5.30. The method of any one of embodiments 13-28, wherein (i) thecondition is lung cancer, and (ii) the one or more non-excluded sequencereads detected above a threshold are sequence reads for cfRNA moleculesderived from one or more genes selected from the group consisting of:ROS1, NKX2-1, GGTLC1, SLC34A2, SFTPA2, BPIFA1, SFTA3, GABRG1, AGR2,GNAT3, MUC5B, SMIM22, CXCL17, and WFDC2.31. The method of any one of embodiments 13-28, wherein (i) thecondition is breast cancer, and (ii) the one or more non-excludedsequence reads detected above a threshold are sequence reads for cfRNAmolecules derived from one or more genes selected from the groupconsisting of: SCGB2A2, CSN1S1, VTCN1, FABP7, LALBA, RNU1-1, OPN1SW,CASP14, KLK5, and WFDC2.32. The method of any one of embodiments 13-28, wherein (i) thecondition is breast cancer, and (ii) the one or more non-excludedsequence reads detected above a threshold are sequence reads for cfRNAmolecules derived from one or more genes selected from the groupconsisting of: CASP14, CRABP2, FABP7, SCGB2A2, SERPINB5, TRGV10, VGLL1,TFF1, and AC007563.5.33. The method of any one of embodiments 13-28, wherein (i) thecondition is liver disease, and (ii) the one or more non-excludedsequence reads detected above a threshold are sequence reads for cfRNAmolecules derived from one or more genes selected from the groupconsisting of: AKR1B10, C3, and PIEXO2.34. The method of any one of embodiments 13-33, further comprisingselecting a treatment based on the condition detected.35. The method of embodiment 34, wherein the condition is cancer, andthe treatment comprises surgical resection, radiation therapy, oradministering an anti-cancer agent.36. The method of embodiment 34 or 35, wherein the method furthercomprises treating the subject with the selected treatment.37. A method of detecting cancer in a subject, the method comprising:

(a) measuring a plurality of target cell-free RNA (cfRNA) molecules in asample of the subject, wherein the plurality of target cfRNA moleculesare selected from one or more transcripts of Tables 1-7; and

(b) detecting the cancer, wherein detecting the cancer comprisesdetecting one or more of the target cfRNA molecules above a thresholdlevel.

38. The method of embodiment 37, wherein the plurality of target cfRNAmolecules are selected from at least 5, 10, 15, or 20 transcripts ofTables 1-7.39. The method of embodiment 37, wherein the plurality of target cfRNAmolecules comprise a plurality of transcripts from Table 1, from each ofTable 2 and 5, or from each of Tables 3-4 and 6.40. The method of any one of embodiments 37-39, wherein the plurality oftarget cfRNA molecules comprise all of the transcripts of one or more ofTables 1, 2, 3, 4, 5, or 6.41. The method of embodiment 37, wherein the plurality of target cfRNAmolecules comprise transcripts from one or more of Tables 1-6 and one ormore transcripts from Table 7.42. The method of embodiment 37, wherein the plurality of target cfRNAmolecules detected above a threshold are cfRNA molecules derived from aplurality of genes selected from the group consisting of: AGR2, BPIFA1,CASP14, CSN1S1, DISP2, EIF2D, FABP7, GABRG1, GNAT3, GRHL2, HOXC10,IDI2-AS1, KRT16P2, LALBA, LINC00163, NKX2-1, OPN1SW, PADI3, PTPRZ1,ROS1, S100A7, SCGB2A2, SERPINB5, SFTA3, SFTPA2, SLC34A2, TFF1, VTCN1,WFDC2, MUC5B, SMIM22, CXCL17, RNU1-1, and KLK5.43. The method of embodiment 37, wherein (i) the cancer is lung cancer,and (ii) the plurality of target cfRNA molecules detected above athreshold are cfRNA molecules derived from a plurality of genes selectedfrom the group consisting of: ROS1, NKX2-1, GGTLC1, SLC34A2, SFTPA2,BPIFA1, SFTA3, GABRG1, AGR2, GNAT3, MUC5B, SMIM22, CXCL17, and WFDC2.44. The method of embodiment 37, wherein (i) the cancer is breastcancer, and (ii) the plurality of target cfRNA molecules detected abovea threshold are cfRNA molecules derived from a plurality of genesselected from the group consisting of: SCGB2A2, CSN1S1, VTCN1, FABP7,LALBA, RNU1-1, OPN1SW, CASP14, KLK5, and WFDC2.45. The method of embodiment 37, wherein (i) the cancer is breastcancer, and (ii) the plurality of target cfRNA molecules detected abovea threshold are cfRNA molecules derived from a plurality of genesselected from the group consisting of: CASP14, CRABP2, FABP7, SCGB2A2,SERPINB5, TRGV10, VGLL1, TFF1, and AC007563.5.46. The method of any one of embodiments 37-45, wherein the measuringcomprises sequencing, microarray analysis, reverse transcription PCR,real-time PCR, quantitative real-time PCR, digital PCR, digital dropletPCR, digital emulsion PCR, multiplex PCR, hybrid capture,oligonucleotide ligation assays, or any combination thereof.47. The method of any one of embodiments 37-46, wherein the measuringcomprises sequencing cfRNA molecules to produce cfRNA sequence reads.48. The method of embodiment 47, wherein sequencing the cfRNA moleculescomprises whole transcriptome sequencing.49. The method of embodiment 47 or 48, wherein sequencing the cfRNAmolecules comprises reverse transcription to produce cDNA molecules, andsequencing the cDNA molecules to produce the cfRNA sequence reads.50. The method of embodiment 47, wherein sequencing the cfRNA moleculescomprises enriching for the target cfRNA molecules or cDNA moleculesthereof.51. The method of any one of embodiments 37-50, wherein the samplecomprises a biological fluid.52. The method of embodiment 51, wherein the biological comprises blood,plasma, serum, urine, saliva, pleural fluid, pericardial fluid,cerebrospinal fluid (CSF), peritoneal fluid, or any combination thereof.53. The method of embodiment 51, wherein the biological comprises blood,a blood fraction, plasma, or serum of the subject.54. The method of any one of embodiments 37-53, wherein detecting one ormore of the target cfRNA molecules above a threshold level comprises (i)detection, (ii) detection above background, or (iii) detection at alevel that is greater than a level of the target cfRNA molecules insubjects that do not have the condition.55. The method of any one of embodiments 37-53, wherein detecting one ormore of the target cfRNA molecules above a threshold level comprisesdetecting the one or more target cfRNA molecules at a level that is atleast about 10 times greater than a level in subjects that do not havethe condition.56. The method of any one of embodiments 47-53, wherein detecting one ormore of the target cfRNA molecules above a threshold level comprisesdetection above a threshold value of 0.5 to 5 reads per million (RPM).57. The method of any one of embodiments 37-53, wherein detecting one ormore of the target cfRNA molecules above a threshold level comprises:

(a) determining an indicator score for each target cfRNA molecule bycomparing the expression level of each of the target cfRNA molecules toan RNA tissue score matrix;

(b) aggregating the indicator scores for each target cfRNA molecule;and,

(c) detecting the cancer when the indicator score exceeds a thresholdvalue.

58. The method of any one of embodiments 47-57, wherein detecting one ormore of the target cfRNA molecules above a threshold level comprisesinputting the sequence reads into a machine learning or deep learningmodel.59. The method of embodiment 58, wherein the machine learning or deeplearning model comprises logistic regression, random forest, gradientboosting machine, Naïve Bayes, neural network, or multinomialregression.60. The method of embodiment 58, wherein the machine learning or deeplearning model transforms the values of the one or more features to thedisease state prediction for the subject through a function comprisinglearned weights.61. The method of any one of embodiments 37-60, wherein the cancercomprises:

(i) a carcinoma, a sarcoma, a myeloma, a leukemia, a lymphoma, ablastoma, a germ cell tumor, or any combination thereof;

(ii) a carcinoma selected from the group consisting of adenocarcinoma,squamous cell carcinoma, small cell lung cancer, non-small-cell lungcancer, nasopharyngeal, colorectal, anal, liver, urinary bladder,testicular, cervical, ovarian, gastric, esophageal, head-and-neck,pancreatic, prostate, renal, thyroid, melanoma, and breast carcinoma;

(iii) hormone receptor negative breast carcinoma or triple negativebreast carcinoma;

(iv) a sarcoma selected from the group consisting of: osteosarcoma,chondrosarcoma, leiomyosarcoma, rhabdomyosarcoma, mesothelial sarcoma(mesothelioma), fibrosarcoma, angiosarcoma, liposarcoma, glioma, andastrocytoma;

(v) a leukemia selected from the group consisting of myelogenous,granulocytic, lymphatic, lymphocytic, and lymphoblastic leukemia; or

(vi) a lymphoma selected from the group consisting of: Hodgkin'slymphoma and Non-Hodgkin's lymphoma.

62. The method of any one of embodiments 37-61, wherein detecting thecancer comprises determining a cancer stage, determining cancerprogression, determining a cancer type, determining cancer tissue oforigin, or a combination thereof.63. The method of any one of embodiments 37-62, further comprisingselecting a treatment based on the cancer detected.64. The method of embodiment 63, wherein the treatment comprisessurgical resection, radiation therapy, or administering an anti-canceragent.65. The method of embodiment 63 or 64, wherein the method furthercomprises treating the subject with the selected treatment.66. A method of identifying cancer biomarkers in samples collected fromone or more subjects, the method comprising:

(a) sequencing cfRNA of a biological fluid collected from subjectswithout cancer to produce non-cancer sequencing reads;

(b) for a plurality of matched samples collected from one or moresubjects with a cancer:

-   -   (i) sequencing DNA and RNA collected from a cancer tissue of a        matched sample to produce sequencing reads for the cancer        tissue;    -   (ii) sequencing cfDNA and cfRNA collected from a matched        biological fluid of the matched sample to produce sequencing        reads for the matched biological fluid;    -   (iii) measuring a tumor fraction by relating counts of cfDNA        sequencing reads for the matched biological fluid to        corresponding counts of DNA sequencing reads for the cancer        tissue; and    -   (iv) measuring tumor content for one or more candidate        biomarkers by multiplying a count of the RNA sequencing reads        for the one or more candidate biomarkers by the tumor fraction,        wherein the one or more candidate biomarkers are expressed at a        higher level in the matched biological fluid than in the        biological fluid collected from the subjects without cancer;

(c) modeling expression of the one or more candidate biomarkers in cfRNAusing the tumor content as a covariate; and

(d) identifying one or more cfRNA cancer biomarkers from among the oneor more candidate biomarkers based on the modeling.

67. The method of embodiment 66, wherein the method further comprises:selectively measuring expression of the one or more cancer biomarkers ina biological fluid of a test subject.68. The method of embodiment 66, wherein the method further comprises:sequencing cfRNA from a biological fluid of a test subject, andgenerating an output for the test subject based on levels of the one ormore cancer biomarkers, wherein the output indicates: a presence ofcancer, determines a cancer stage, monitors a cancer progression, ordetermines a cancer type.69. The method of embodiment 68, wherein a machine learning or deeplearning model transforms values for sequencing reads of the cfRNA ofthe test subject to the output for the test subject through a functioncomprising learned weights.70. The method of embodiment 68, further comprising selecting a cancertreatment for the test subject, and optionally administering thetreatment to the test subject.71. The method of any one of embodiments 66-70, wherein the modelingcomprises negative binomial general linear model analysis (NB-GLM).72. The method of any one of embodiments 66-70, wherein the modeling isperformed using a computer-implemented classification model whichapplies at least one of a leave-one-out (LOO) or k-fold cross validationclassification to classify different cancer features, wherein k-fold isat least 5-fold.73. The method of any one of embodiments 66-71, wherein the modelingcomprises inputting the one or more DNA, RNA cfDNA or cfRNA sequencesinto a machine learning or deep learning model.74. The method of embodiment 73, wherein the machine learning or deeplearning model comprises logistic regression, random forest, gradientboosting machine, Naïve Bayes, neural network, or multinomialregression.75. A computer system for implementing one or more steps in the methodof any one of embodiments 1-74.76. A non-transitory, computer-readable medium, having stored thereoncomputer-readable instructions for implementing one or more steps in themethod of any one of embodiments 1-74.

EXAMPLES

It is understood that the examples and embodiments described herein arefor illustrative purposes only and that various modifications or changesin light thereof will be suggested to persons skilled in the art and areto be included within the spirit and purview of this application andscope of the appended claims.

Example 1: Detection of Tissue-Specific RNA in the Plasma of CancerPatients

Cell-free RNA (cfRNA) is a promising analyte for cancer detection, but acomprehensive assessment of cfRNA is lacking. To characterizetumor-derived RNA in plasma, we performed an exploratory analysis from aCirculating Cell-free Genome Atlas (CCGA) substudy to examine cfRNAexpression in participants with and without cancer. This analysisfocused on breast, lung, and colorectal cancers due to their highincidence in the general population and in CCGA.

We selected 210 participants from the CCGA training set (Klein et al.,ASCO, 2018). A total of 98 participants were diagnosed with stage IIIcancer at the time of blood draw (breast (47 patients), lung (32patients), colorectal (15 patients), and anorectal (4 patients)). StageIII samples were selected to maximize signal in the blood and avoidconfounding signal from potential secondary metastases. 112 non-cancerparticipants frequency-age-matched to the cancer group were alsoincluded. For each participant, whole transcriptome libraries from buffycoat, cfRNA, and FFPE of tumor tissue biopsies were generated.

Nucleic acids were extracted from participant plasma, samples wereDNAse-treated to remove cell-free DNA (cfDNA) and genomic DNA, andreverse transcription was performed using random hexamer primers tocapture the whole transcriptome for each study participant. Theresulting cDNA was converted into DNA libraries, amplified, and depletedof abundant sequences arising from ribosomal, mitochondrial, andblood-related transcripts, such as globins. The resultingwhole-transcriptome RNA-seq libraries were sequenced at a depth of ˜750Mpaired-end reads per sample and analyzed using a custom bioinformaticspipeline that generated UMI-collapsed counts for each gene on asample-by-sample basis. This same procedure was used to create andanalyze RNA-seq libraries from matched buffy coat and tissue RNA whenavailable. Due to the presence of residual DNA contamination, alldownstream analyses relied on the use of strict RNA reads, defined inthis example as read pairs where at least one read overlapped anexon-exon junction. FIG. 11 shows a summary of the end-to-end workflow.Table 9 provides a summary of participant samples:

TABLE 9 Disease Status Passed QC cfRNA WBC Tissue Breast Fail 1 0 0 LungFail 2 1 0 Non-cancer Fail 4 0 0 Anorectal Pass 4 1 4 Breast Pass 46 3240 Colorectal Pass 15 11 10 Lung Pass 30 26 12 Non-cancer Pass 89 93 0Young Healthy Pass 19 19 0 Total NA 210 183 66

We compared our data to RNA samples from TCGA (FIG. 12A). When weprojected CCGA tumor tissue RNA-seq data onto the principal componentsderived from TCGA tumor tissue RNA-seq data, the CCGA tumor tissuesamples were separable by cancer type (FIG. 12B). These results suggestthat the expression profiles of CCGA and TCGA tumors were very similarin spite of differences in sample collection/handling/librarypreparation, and validate the analytical approach. A projection ofcancer cfRNA samples from the CCGA cohort onto the principal componentsderived from TCGA tumor tissue RNA-seq data showed no separation of thesample by cancer type (FIG. 12C), implying that cancer type was not thedominant source of variance in cfRNA.

The majority of cfRNA in plasma is thought to originate from healthyimmune cells. As such, we treated these transcripts as background noiseand focused on tumor-derived cfRNA as a source of cancer signal. Ouranalysis identified two classes of genes in cfRNA data: “dark channels”and “dark channel biomarkers”. Dark channels are genes that were notdetected in the cfRNA of non-cancer participants. Of 57,783 annotatedgenes, 39,564 (68%) were identified as dark channels. Dark channelbiomarker (DCB) genes met three criteria: 1) median expression of thegene in the non-cancer cohort was zero, 2) gene expression was detectedin more than one participant in the cancer cohort, and 3) geneexpression was up-regulated in the cancer group.

14 DCB genes were identified for lung cancer: SLC34A2, GABRG1, ROS1,AGR2, GNAT3, SFTPA2, MUC5B, SFTA3, SMIM22, CXCL17, BPIFA1, WFDC2,NKX2-1, and GGTLC1 (see Table 2). 10 DCB genes were identified forbreast cancer: RNU1-1, CSN1S1, FABP7, OPN1SW, SCGB2A2, LALBA, CASP14,KLK5, WFDC2, and VTCN1 (see Table 3). No DCB genes were identified forcolorectal cancer.

DCB genes exhibited several distinct characteristics. First, DCB geneswere enriched for tissue-specific genes (FIG. 13 ). Among the 57,783annotated genes, 0.3% were lung-specific and 0.2% were breast-specific.In comparison, 50% of the lung DCB genes were lung-specific, and 44% ofthe breast DCB genes were breast-specific (as defined by the proteinatlas database (Uhlén et al., Science, 2015)).

Moreover, some DCB genes were subtype-specific biomarkers that were onlydetected in certain cancer subtypes (FIGS. 14A and 14B). FABP7 was onlydetected in triple negative breast cancer (TNBC) samples. Conversely,SCGB2A2 was not detected in TNBC, but was detected in HER2+ and HR+/HER−breast cancer samples. SLC34A2, ROS1, SFTPA2 and CXCL17 genes weredetected in cfRNA of lung adenocarcinoma patient samples but not insquamous cell carcinoma patient samples. These subtype-specific genesalso had higher expression in tumor tissue compared to other subtypes ofcancer originating from the same organ.

In order to determine the source of tumor-associated transcripts in theblood, concordance between cfRNA and tumor tissue RNA for dark channelbiomarker genes was assessed. High concordance between cfRNA and tumortissue expression was observed (FIG. 15A). Genes not detected in thetumor tissue were unlikely to be detected in the matched cfRNA sample,and genes detected in the tumor tissue were more likely to be detectedin the matched cfRNA sample. Additionally, tumor content, measured asthe product of cfDNA tumor fraction for a given patient and the geneexpression in matched tumor tissue, was a strong predictor of thedetectability of a DCB gene in the cfRNA of breast cancer patients (FIG.15B).

Dark channel biomarkers (DCBs), transcripts that were not found in cfRNAfrom non-cancer subjects, exhibited the potential for highsignal-to-noise in cancer patients. DCB signal was correlated with tumorcontent (measured as the product of tumor fraction in the blood and RNAexpression in the tissue). cfRNA DCBs were identified in cancerparticipants in a tissue- and subtype-specific manner. We observed caseswhere high tumor tissue expression led to DCB signal amplification andenabled detection of cancer in patients with low cfDNA tumor fraction.Taken together, these data suggest that tissue-specific transcripts havepotential for use in blood-based multi-cancer detection.

Example 2: Identifying Biomarkers in Heterogeneous Samples

We observed two common sources of false-positives in biomarker discoveryon heterogeneous samples using standard differential expression (DE)analysis. First, the gene expression follows bimodal distribution due togenetic heterogeneity or gene amplification drop-out in both control andcancer groups. Second, a single influential outlier inflated the slopeand p-value of the generalized linear model (GLM).

A method was developed to identify differentially expressed genes inhighly heterogeneous samples, such as cfRNA based on tissue expression,referred to as heteroDE. The heteroDE model uses a negative binomialgeneralized linear moodel (NB-GLM). To reduce the false-positives,heteroDE includes two additional functionalities: (1) it checks if thegene expression in the non-cancer group follows bimodal distribution dueto genetic heterogeneity or gene amplification drop-out; and (2) itchecks if only a single outlier sample is influencing the p-value of theNB-GLM. The outlier sample is identified using Cook's distance. TheNB-GLM is performed for a second time without the sample with thelargest Cook's distance.

In contrast to prior differential expression (DE) methods, heteroDE usesthe tumor content as a covariate in the NG-GLM. The tumor content forthe non-cancer samples was set to zero. The hypothesis for a cfRNA tumorbiomarker gene was that the higher of the gene's expression in thetissue and the larger the tumor fraction in the cfDNA, the more likelyit is to detect that gene in cfRNA. When we applied this method tobreast cancer samples, we identified 9 cfRNA biomarkers: TRGV10,SCGB2A2, CASP14, FABP7, CRABP2, VGLL1, SERPINB5, TFF1, and AC007563.5(see Table 4). Three of these biomarkers (FABP7, SCGB2A2, CASP14)overlap with the genes identified as DCB genes.

An example workflow illustrating the sample processing and parameterdetermination in accordance with heteroDE is shown in FIG. 19 . Tumorcontent was constrained to zero for non-cancer subjects, due to a lackof tissue sample. An example implementation of the workflow is given by:

-   -   K_(i,j): read counts for gene i in the cfRNA of patient j;    -   μ_(i,j): mean read counts for gene i in the cfRNA of patient j;    -   α_(i): dispersion for gene i;    -   γ_(i): the mean reads count when no tumor contents in plasma for        gene i;    -   x_(i,j): tumor contents, log 10 (tumor fraction in matched        cfDNA*gene expression in matched tumor tissue)    -   βi: the coefficient for tumor contents;

K _(i,j) ˜NB(μ_(i,j),α_(i))

log(μ_(i,j))=(γ_(i) +x _(i,j)β_(i))

Feature selection using an information gain method was also tested.Information gain is a method to select genes with high mutualinformation between the binarized cfRNA gene expression and thecancer/non-cancer label. The gene expression RPM matrix was converted toa binary matrix. If the gene had an RPM>0, it was converted to 1. If thegene had an RPM=0, it was set to 0. The information gain was computedfor each gene given the cancer type (e.g., lung cancer) and non-cancerlabel using the binary expression value. The non-cancer group for thebreast cancer group was balanced with gender—only the female subjects inthe non-cancer group were selected. The top 100 genes with the highestinformation gain were selected as the feature for modeling. The value ofeach gene was converted to binary value in the modeling process. Theseprocedures were repeated for breast cancer vs. non-cancer, andcolorectal cancer vs. non-cancer. The top 30 genes with the highestinformation gain forlung cancer are shown in Table 5, and the top 30genes with the highest information gain for breast cancer are shown inTable 6.

In another embodiment, feature selection was carried out from cancertissue samples to identify genes expressed in cancer tissues samples butnot expressed in non-cancer participants. Libraries were prepared andsequenced as described above in Example 1. For each cancer tissuesample, we identified genes that were expressed at relatively highlevels in cancer tissue (tissue RPM>10) from Dark Channels. These geneswere classified as “tissue bright channel genes.” The top 15 tissuebright channel genes identified are shown in Table 7.

Example 3: Validation of DCB's in a Separate Cohort

We set out to validate the DCBs identified in our CCGA cohort in anorthogonal set of breast (38) and lung (18) cancer samples obtained froma commercial vendor (Discovery Life Sciences). Stage I-IV patients wereselected to assess the prevalence of DCBs across disease progression,and 38 age-matched non-cancer samples were included as controls of DCBexpression in patients without cancer. In order to improve sensitivityand reduce sequencing requirements, we developed a targeted enrichmentapproach to select for 23 DCBs identified in our CCGA cohort. We alsoenriched for 33 positive control genes that are normally present innon-cancer plasma. These transcripts act as carrier material in theenrichment step, since the majority of non-cancer samples will notcontain DCB transcripts. The resulting targeted RNA-seq libraries weresequenced and subsampled to a depth of 100M paired-end reads per sample,and the number of strict RNA reads quantified for both target andoff-target genes. When compared to the whole transcriptome assay, wefound that the targeted approach increased conversion efficiency fortargeted cfRNA transcripts by 2- to 3-fold.

Of the 23 DCBs identified in our CCGA cohort, all but one (CRABP2) had amedian expression (in RPM) of 0 in the non-cancer group. 19 DCBs in ourpanel were expressed in at least 1 cancer sample in the validationcohort (≥2 unique fragments), and 16 of these DCBs were differentiallyexpressed in at least one cancer type compared to non-cancer samples.With the increased assay efficiency and stage, we noticed that sometissue-specific markers are present in both breast and lung cancer,though they remain differentially expressed between the two groups.There are also some DCBs that are exclusively expressed in one cancertype, like SCGB2A2 in breast cancer, and ROS1, SFTA3, and SFTPA2 in lungcancer. For all of the DCBs observed in this validation cohort, thelevel of DCB expression in cancer samples increased with stage, with thehighest expression seen for stage IV samples in our cohort, supportingthe validity of these features as specific markers of cancer. Despitethis trend, we also observed DCB expression in early stage cancerswithin our cohort, suggesting an opportunity to detect early stagecancers using an approach that enriches for DCBs. Illustrative resultsare shown in FIGS. 16A-D, with the number of read counts along they-axis.

Example 4: Classification Results

We applied leave-one-out (LOO) and 5-fold cross validationclassification using different feature selection methods, includingdark-channel biomarkers (DCB), heteroDE, and information gain (IG).Illustrative workflows are shown in FIGS. 17A-B. Because heteroDEutilized matched tumor tissue, this feature selection method was notapplied to lung cancer/non-cancer classification due to limited numberof lung tissue samples. Overall, LOO had significantly betterclassification performance in LOO compared to 5-fold cross validation inbreast cancer/non-cancer classification, implying that the breast cancerclassifier is under trained in 5-fold classification due to smallersample sizes in each training set. DCB had the best performance(sensitivity at 98% specificity: 0.2±0.037) for lung cancer/non-cancerclassifier and heteroDE had the best performance (sensitivity at 98%specificity: 0.303±0.046) for breast cancer/non-cancer classifier (Table10).

TABLE 10 Cancer Type Feature Selection Cross-Validation Sens95spec LungDCB LOO  0.3 ± 0.042 Lung IG LOO 0.333 ± 0.043 Breast heteroDE LOO 0.394± 0.049 Breast DCB LOO 0.212 ± 0.041 Breast IG LOO 0.303 ± 0.046 LungDCB 5-fold 0.261 ± 0.146 Breast heteroDE 5-fold 0.177 ± 0.142

Illustrative results are also plotted in FIGS. 18A-C, which weregenerated using leave-one-out cross validation. FIG. 18A shows areceiver operating characteristic (ROC) plot and a variable importanceplot from leave-one-out (LOO) cross-validation classification for breastvs non-cancer using the heteroDE feature selection method and a randomforest classifier. The input data was counts per gene which wasnormalized using size factor normalization (using theestimateSizeFactors) function from the DESeq2 R package). As shown inTable 10, the sensitivity at 95% was 0.394+/−0.049.

FIG. 18B shows a ROC plot from leave-one-out (LOO) cross-validationclassification for lung vs non-cancer labels using the dark channelfeature selection method and a random forest classifier. The input datawas normalized counts per gene in reads per million (rpm). As shown inTable 10, the sensitivity at 95% specificity was 0.3+/−0.042.

FIG. 18C shows a ROC plot and variable importance plot fromleave-one-out (LOO) cross-validation classification for breast vsnon-cancer labels using the dark channel feature selection method and arandom forest classifier. The input data was normalized counts per genein reads per million (rpm). As shown in Table 10, the sensitivity at 95%specificity was 0.212+/−0.041.

Example 5: Materials and Methods

Sequencing Data Processing

Raw reads were aligned to gencode v19 primary assembly with alltranscripts using STAR version 2.5.3a. Duplicate sequence reads weredetected and removed based on genomic alignment position and non-randomUMI sequences. A majority of paired-end reads had UMI sequences exactlymatching expected sequences. A subset of reads contained errors in theUMI sequence and a heuristic error correction was applied. If the UMIwas within a hamming distance of 1 from an expected UMI, it was assignedto that UMI sequence. In the case where hamming distance exceeded 1, ormultiple known sequences were within a hamming distance of 1, the readwith the UMI error was discarded. Sets of reads sharing alignmentposition and corrected UMIs were error corrected via multiple sequencealignment of member reads and a single consensus sequence/alignment wasgenerated. Read alignments were compared to annotated transcripts ingencode v19. Only reads spanning annotated exon-exon junctions werecounted to the remove false counts resulting from DNA contaminatingreads.

Sample Collection

Whole blood was collected in Streck Cell-free DNA BCT tubes, which wereshipped and stored at ambient temperature prior to plasma separation.Whole blood was spun at 1600 g for 10 min at 4° C. in a swing-bucketrotor to separate plasma. The plasma layer was transferred to a separatetube and spun at 15000 g for 12 min at 4° C. to further remove cellularcontaminants. Double-spun plasma was stored at −80° C. and thawed atroom temperature prior to extraction to avoid the formation ofcryoprecipitates.

Sample Selection Criteria

We selected a subset of stage III breast, lung, and colorectal cancersamples from the Circulating Cell-free Genome Atlas study (CCGA,NCT02889978). We required that the selected patients had at least twotubes of unprocessed grade 1-2 plasma (no hemolysis), with 6-8 mL ofplasma per patient. We further required that selected patients hadmatched cfDNA sequencing data from previous studies. Once the cancerpatients were selected, we selected an equal number of non-cancersamples matched for age, gender, and ethnicity to the cancer samples.Based on this criteria, we selected 210 samples. These samples wererandomized into batches of 14 using a randomization function in R thatensured a random mixture of cancer types (cancer and non-cancer samples)within each batch.

Sample Processing

Cell-free nucleic acids were extracted from up to 8 mL of frozen plasmausing the circulating miRNA protocol from the QIAamp Circulating NucleicAcids kit (Qiagen, 55114). The extracted material was DNase treatedusing the RNase-free DNase Set (Qiagen, 79254) according to themanufacturer's instructions and quantified using the High SensitivityRNA Fragment Analyzer kit (Agilent, DNF-472). Reverse transcription andadapter ligation was performed using the TruSeq RNA Exome kit (Illumina,20020189 The resulting libraries were depleted of abundant sequencesusing the AnyDeplete for Human rRNA and Mitochondrial Kit (Tecan, 9132),supplemented with a custom set of depletion targets.

Sequenced samples were screened and those exhibiting low quality controlmetrics were excluded from subsequent analysis. One assay metric andthree pipeline metrics were chosen as “red flags” and were used toexclude samples with poor metrics. The assay metric measured whethersamples had sufficient material for sequencing, and the pipeline metricswere sequencing depth, RNA purity, and cross-sample contamination.

Gene Expression Quantification

Initial inspection of the data revealed varying levels of residual DNAin cfRNA samples despite the DNase digestion step during librarypreparation. The level of contamination was minimal (<6 haploid genomeequivalents per sample), and was not correlated with the amount of cfDNAprior to digestion or batch-specific issues. Rather, it appears to bestochastic, in line with previous reports.

A QC metric, “quantile 95 strand specificity” defined as the strandspecificity of genes at or below the 95th quantile of expression, wasused to assess the level of DNA contamination in each sample. UHRpositive control samples exhibited high quantile 95 strand specificity(>0.85). cfRNA quantile 95 strand specificity values were spread acrossa wide range (0.52-0.89). For reference, cfDNA samples have a quantile95 strand specificity of ˜0.5, suggesting that some cfRNA samples aredominated by signal from residual DNA. The read strand colors show evendistribution of sense and anti-sense reads in NC67 versus only sensereads in NC3. Additionally, there is abundant coverage across bothintrons and exons in NC67, as would be expected with presence of DNA.The distribution of fragment length in samples with high levels of DNAcontamination shows that they mimic the length distribution of cfDNA(median ˜160), strongly suggesting that undigested cfDNA is the majorcontaminant.

Samples with quantile 95 strand specificity below 0.84 were flagged andremoved from subsequent analysis. To further guard against the inflationof RNA counts due to DNA contamination, the gene counts presented hereare generated using strict counts, defined as read pairs where at leastone of the two reads maps across an exon-exon junction. An experimentperformed using varying levels of cfDNA spiked into a cfRNA sampleshowed that the estimation of RNA levels using strict counts remainsunchanged, supporting the use of strict counts in the pilot studysamples for quantifying and comparing gene expression.

Dark-Channel Features Election

The dark channel genes were identified by the following criteria: 1) Themedian expression (in RPM) of this gene in the non-cancer group is 0,and the standard deviation of this gene is less than 0.1 RPM. The darkchannel biomarkers (DCB) for each cancer type were identified using thefollowing criteria: 1) There are at least two samples in the specifiedcancer group for which the gene is expressed, 2) the RPM of the secondhighest expressed sample is greater than 0.1, and 3) the gene isdifferentially expressed in the specified cancer group compared to thenon-cancer group (p-value <2e-02 for lung cancer and p-value <2e-01 forbreast cancer). The p-value of two-group differential expression wascalculated by the edgeR package. There are 816 genes with FDR <0.05between lung cancer and non-cancer groups. There are 28 genes with FDR<0.05 between breast cancer and non-cancer groups. There are 4 geneswith FDR <0.05 between colorectal cancer and non-cancer groups. For theboxplot and heatmap, we only displayed the most significantdifferentially expressed genes (FDR <2e-06 for lung and breast cancerand FDR <2e-02 for colorectal cancer).

Annotation of tissue-specific genes was performed as follows. Thetissue-specific gene files for lung, breast, and colon cancers weredownloaded from the Human Protein Atlas website (www.proteinatlas.org/).Tissue-specific genes are divided into three categories: 1) TissueEnriched: At least 4-fold higher mRNA levels in a particular tissue ascompared to all other tissues, 2) Group Enriched: At least 4-fold highermRNA levels in a group of 2-5 tissues, 3) Tissue Enhanced: At least4-fold higher mRNA levels in a particular tissue as compared to averagelevels in all tissues. All three categories were included in ourdefinition of tissue-specific genes.

In order to test enrichment of the tissue-specific genes. 1) Fisher'sexact test was applied to test the independence between lung DCB andlung-specific genes for all the annotated human genes. 2) Fisher's exacttest was applied to test the independence between breast DCB andbreast-specific genes for all the annotated human genes.

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References and citations to other documents, such as patents, patentapplications, patent publications, journals, books, papers, webcontents, have been made throughout this disclosure. All such documentsare hereby incorporated herein by reference in their entirety for allpurposes.

Various modifications of the invention and many further embodimentsthereof, in addition to those shown and described herein, will becomeapparent to those skilled in the art from the full contents of thisdocument, including references to the scientific and patent literaturecited herein. The subject matter herein contains important information,exemplification and guidance that can be adapted to the practice of thisinvention in its various embodiments and equivalents thereof. Allreferences cited throughout the specification are expressly incorporatedby reference herein.

The foregoing detailed description of embodiments refers to theaccompanying drawings, which illustrate specific embodiments of thepresent disclosure. Other embodiments having different structures andoperations do not depart from the scope of the present disclosure. Theterm “the invention” or the like is used with reference to certainspecific examples of the many alternative aspects or embodiments of theapplicants' invention set forth in this specification, and neither itsuse nor its absence is intended to limit the scope of the applicants'invention or the scope of the claims. This specification is divided intosections for the convenience of the reader only. Headings should not beconstrued as limiting of the scope of the invention. The definitions areintended as a part of the description of the invention. It will beunderstood that various details of the present invention may be changedwithout departing from the scope of the present invention. Furthermore,the foregoing description is for the purpose of illustration only, andnot for the purpose of limitation.

While the present invention has been described with reference to thespecific embodiments thereof, it should be understood by those skilledin the art that various changes may be made and equivalents may besubstituted without departing from the true spirit and scope of theinvention. In addition, many modifications may be made to adapt to aparticular situation, material, composition of matter, process, processstep or steps, to the objective, spirit and scope of the presentinvention. All such modifications are intended to be within the scope ofthe claims appended hereto.

1.-36. (canceled)
 37. A method of detecting cancer in a subject, themethod comprising: (a) measuring a plurality of target cell-free RNA(cfRNA) molecules in a sample of the subject, wherein the plurality oftarget cfRNA molecules are selected from one or more transcripts ofTables 1-7; and (b) detecting the cancer, wherein detecting the cancercomprises detecting one or more of the target cfRNA molecules above athreshold level.
 38. The method of claim 37, wherein (a) the pluralityof target cfRNA molecules are selected from at least 5, 10, 15, or 20transcripts of Tables 1-7; (b) the plurality of target cfRNA moleculescomprise a plurality of transcripts from Table 1, from each of Table 2and 5, or from each of Tables 3-4 and 6; (c) the plurality of targetcfRNA molecules comprise all of the transcripts of one or more of Tables1, 2, 3, 4, 5, or 6; or (d) the plurality of target cfRNA moleculescomprise transcripts from one or more of Tables 1-6 and one or moretranscripts from Table
 7. 39-41. (canceled)
 42. The method of claim 37,wherein the plurality of target cfRNA molecules detected above athreshold are cfRNA molecules derived from a plurality of genes selectedfrom the group consisting of: AGR2, BPIFA1, CASP14, CSN1S1, DISP2,EIF2D, FABP7, GABRG1, GNAT3, GRHL2, HOXC10, IDI2-AS1, KRT16P2, LALBA,LINC00163, NKX2-1, OPN1SW, PADI3, PTPRZ1, ROS1, S100A7, SCGB2A2,SERPINB5, SFTA3, SFTPA2, SLC34A2, TFF1, VTCN1, WFDC2, MUC5B, SMIM22,CXCL17, RNU1-1, and KLK5.
 43. The method of claim 37, wherein (a): (i)the cancer is lung cancer, and (ii) the plurality of target cfRNAmolecules detected above a threshold are cfRNA molecules derived from aplurality of genes selected from the group consisting of: ROS1, NKX2-1,GGTLC1, SLC34A2, SFTPA2, BPIFA1, SFTA3, GABRG1, AGR2, GNAT3, MUC5B,SMIM22, CXCL17, and WFDC2; (b): (i) the cancer is breast cancer, and(ii) the plurality of target cfRNA molecules detected above a thresholdare cfRNA molecules derived from a plurality of genes selected from thegroup consisting of: SCGB2A2, CSN1S1, VTCN1, FABP7, LALBA, RNU1-1,OPN1SW, CASP14, KLK5, and WFDC2; or (c): (i) the cancer is breastcancer, and (ii) the plurality of target cfRNA molecules detected abovea threshold are cfRNA molecules derived from a plurality of genesselected from the group consisting of: CASP14, CRABP2, FABP7, SCGB2A2,SERPINB5, TRGV10, VGLL1, TFF1, and AC007563.5. 44-45. (canceled)
 46. Themethod of claim 37, wherein the measuring comprises sequencing,microarray analysis, reverse transcription PCR, real-time PCR,quantitative real-time PCR, digital PCR, digital droplet PCR, digitalemulsion PCR, multiplex PCR, hybrid capture, oligonucleotide ligationassays, or any combination thereof.
 47. The method of claim 37, whereinthe measuring comprises sequencing the cfRNA molecules to produce cfRNAsequence reads, and the sequencing the cfRNA molecules optionallycomprises (i) whole transcriptome sequencing; (ii) reverse transcriptionto produce cDNA molecules, followed by sequencing the cDNA molecules toproduce the cfRNA sequence reads; or (iii) enriching for the targetcfRNA molecules or cDNA molecules thereof. 48-50. (canceled)
 51. Themethod of claim 37, wherein the sample comprises a biological fluid,wherein the biological fluid is optionally blood, a blood fraction,plasma, serum, urine, saliva, pleural fluid, pericardial fluid,cerebrospinal fluid (CSF), peritoneal fluid, or a combination thereof.52-53. (canceled)
 54. The method of claim 37, wherein detecting one ormore of the target cfRNA molecules above a threshold level comprises:(a): (i) detection, (ii) detection above background, or (iii) detectionat a level that is greater than a level of the target cfRNA molecules insubjects that do not have the condition; (b): detecting the one or moretarget cfRNA molecules at a level that is at least about 10 timesgreater than a level in subjects that do not have the condition; or (c):detection above a threshold value of 0.5 to 5 reads per million (RPM).55-56. (canceled)
 57. The method of claim 37, wherein detecting one ormore of the target cfRNA molecules above a threshold level comprises:(a) determining an indicator score for each target cfRNA molecule bycomparing the expression level of each of the target cfRNA molecules toan RNA tissue score matrix; (b) aggregating the indicator scores foreach target cfRNA molecule; and, (c) detecting the cancer when theindicator score exceeds a threshold value.
 58. The method of claim 47,wherein detecting one or more of the target cfRNA molecules above athreshold level comprises inputting the sequence reads into a machinelearning or deep learning model; optionally wherein (a) the machinelearning or deep learning model comprises logistic regression, randomforest, gradient boosting machine, Naïve Bayes, neural network, ormultinomial regression, or (b) the machine learning or deep learningmodel optionally transforms values of one or more features to a diseasestate prediction for the subject through a function comprising learnedweights. 59-60. (canceled)
 61. The method of claim 37, wherein thecancer comprises: (i) a carcinoma, a sarcoma, a myeloma, a leukemia, alymphoma, a blastoma, a germ cell tumor, or any combination thereof;(ii) a carcinoma selected from the group consisting of adenocarcinoma,squamous cell carcinoma, lung cancer, nasopharyngeal, colorectal, anal,liver, urinary bladder, testicular, cervical, ovarian, gastric,esophageal, head-and-neck, pancreatic, prostate, renal, thyroid,melanoma, and breast carcinoma; (iii) hormone receptor negative breastcarcinoma or triple negative breast carcinoma; (iv) a sarcoma selectedfrom the group consisting of: osteosarcoma, chondrosarcoma,leiomyosarcoma, rhabdomyosarcoma, mesothelial sarcoma (mesothelioma),fibrosarcoma, angiosarcoma, liposarcoma, glioma, and astrocytoma; (v) aleukemia selected from the group consisting of myelogenous,granulocytic, lymphatic, lymphocytic, and lymphoblastic leukemia; or(vi) a lymphoma selected from the group consisting of: Hodgkin'slymphoma and Non-Hodgkin's lymphoma.
 62. The method of claim 37, whereindetecting the cancer comprises determining a cancer stage, determiningcancer progression, determining a cancer type, determining cancer tissueof origin, or a combination thereof.
 63. The method of claim 37, furthercomprising selecting a treatment based on the cancer detected, andoptionally treating the subject with the selected treatment.
 64. Themethod of claim 63, wherein the treatment comprises surgical resection,radiation therapy, or administering an anti-cancer agent.
 65. (canceled)66. A method of identifying cancer biomarkers in samples collected fromone or more subjects, the method comprising: (a) sequencing cfRNA of abiological fluid collected from subjects without cancer to producenon-cancer sequencing reads; (b) for a plurality of matched samplescollected from one or more subjects with a cancer: (i) sequencing DNAand RNA collected from a cancer tissue of a matched sample to producesequencing reads for the cancer tissue; (ii) sequencing cfDNA and cfRNAcollected from a matched biological fluid of the matched sample toproduce sequencing reads for the matched biological fluid; (iii)measuring a tumor fraction by relating counts of cfDNA sequencing readsfor the matched biological fluid to corresponding counts of DNAsequencing reads for the cancer tissue; and (iv) measuring tumor contentfor one or more candidate biomarkers by multiplying a count of the RNAsequencing reads for the one or more candidate biomarkers by the tumorfraction, wherein the one or more candidate biomarkers are expressed ata higher level in the matched biological fluid than in the biologicalfluid collected from the subjects without cancer; (c) modelingexpression of the one or more candidate biomarkers in cfRNA using thetumor content as a covariate; and (d) identifying one or more cfRNAcancer biomarkers from among the one or more candidate biomarkers basedon the modeling.
 67. The method of claim 66, wherein the method furthercomprises: (a) selectively measuring expression of the one or morecancer biomarkers in a biological fluid of a test subject; or (b)sequencing cfRNA from a biological fluid of a test subject, andgenerating an output for the test subject based on levels of the one ormore cancer biomarkers, wherein the output indicates: a presence ofcancer, determines a cancer stage, monitors a cancer progression, ordetermines a cancer type.
 68. (canceled)
 69. The method of claim 67,wherein a machine learning or deep learning model transforms values forsequencing reads of the cfRNA of the test subject to the output for thetest subject through a function comprising learned weights.
 70. Themethod of claim 67, further comprising selecting a cancer treatment forthe test subject, and optionally administering the treatment to the testsubject.
 71. The method of claim 66, wherein the modeling (i) comprisesnegative binomial general linear model analysis (NB-GLM); (ii) isperformed using a computer-implemented classification model whichapplies at least one of a leave-one-out (LOO) or k-fold cross validationclassification to classify different cancer features, wherein k-fold isat least 5-fold; or (iii) comprises inputting the one or more DNA, RNAcfDNA or cfRNA sequences into a machine learning or deep learning model,wherein the machine learning or deep learning model optionally compriseslogistic regression, random forest, gradient boosting machine, NaïveBayes, neural network, or multinomial regression. 72-76. (canceled)